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You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: She evaluates 3,000 German verbs with a token frequency between 10 and 2,000 against the Duden (Dudenredaktion 2001). The largest lexical evaluation we know of is that of Schulte im Walde (2002b) for German. However, their evaluation does not examine the extracted subcategorization frames but rather the argument–adjunct distinctions posited by their system. Citation Sentence: She evaluates 3,000 German verbs with a token frequency between 10 and 2,000 against the Duden ( Dudenredaktion 2001 ) . Context after the citation: We will refer to this work and the methods and results presented by Schulte im Walde again in Sections 6.2 and 6.3. We carried out a large-scale evaluation of our automatically induced lexicon (2,993 active verb lemmas for Penn-II and 3,529 for Penn-III, as well as 1,422 passive verb lemmas from Penn-II) against the COMLEX resource. To our knowledge this is the most extensive evaluation ever carried out for English lexical extraction. We conducted a number of experiments on the subcategorization frames extracted from Penn-II and Penn-III which are described and discussed in Sections 6.2, 6.3, and 6.4.

Background

acl_arc_intent_classification:train:2

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Collins & Duffy (2002) showed how the perceptron algorithm can be used to efficiently compute the best parse with DOP1's subtrees, reporting a 5.1% relative reduction in error rate over the model in Collins (1999) on the WSJ. Although Bod's method obtains very competitive results on the Wall Street Journal (WSJ) task, the parsing time was reported to be over 200 seconds per sentence (Bod 2003). Bod (2001) used an alternative technique which samples a fixed number of subtrees of each depth and which has the effect of assigning roughly equal weight to each node in the training data. Citation Sentence: Collins & Duffy ( 2002 ) showed how the perceptron algorithm can be used to efficiently compute the best parse with DOP1 's subtrees , reporting a 5.1 % relative reduction in error rate over the model in Collins ( 1999 ) on the WSJ . Context after the citation: Goodman (2002) furthermore showed how Bonnema et al.'s (1999) and Bod's (2001) estimators can be incorporated in his PCFGreduction, but did not report any experiments with these reductions. This paper presents the first published results with Goodman's PCFG-reductions of both Bonnema et al.'s (1999) and Bod's (2001) estimators on the WSJ. We show that these PCFG-reductions result in a 60 times speedup in processing time w.r.t. Bod (2001, 2003). But while Bod's estimator obtains state-of-the-art results on the WSJ, comparable to Charniak (2000) and Collins (2000), Bonnema et al.'s estimator performs worse and is comparable to Collins (1996).

Background

acl_arc_intent_classification:train:20

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Figure 2(a) shows the frame-based semantic representation for the utterance "What time is Analyze This playing 2 See (Nakatani and Chu-Carroll, 2000) for how MIMIC's dialoguelevel knowledge is used to override default prosodic assignments for concept-to-speech generation. MIMIC utilizes a non-recursive frame-based semantic representation commonly used in spoken dialogue systems (e.g. (Seneff et al., 1991; Lamel, 1998)), which represents an utterance as a set of attribute-value pairs. Citation Sentence: Figure 2 ( a ) shows the frame-based semantic representation for the utterance `` What time is Analyze This playing 2 See ( Nakatani and Chu-Carroll , 2000 ) for how MIMIC 's dialoguelevel knowledge is used to override default prosodic assignments for concept-to-speech generation . Context after the citation: in Montclair?" MIMIC's semantic representation is constructed by first extracting, for each attribute, a set of keywords from the user utterance. Using a vector-based topic identification process (Salton, 1971; Chu-Carroll and Carpenter, 1999), these keywords are used to determine a set of likely values (including null) for that attribute. Next, the utterance is interpreted with respect to the dialogue history and the system's domain knowledge.

Background

acl_arc_intent_classification:train:200

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: For Berkeley system, we use the reported results from Durrett and Klein (2014). Results for HOTCoref are slightly different from the results reported in Bj¨orkelund and Kuhn (2014). 12We do not provide results from Berkeley and HOTCoref on ACE-2004 dataset as they do not directly support ACE input. Citation Sentence: For Berkeley system , we use the reported results from Durrett and Klein ( 2014 ) . Context after the citation: shows significant improvement on all metrics for both datasets. Existing systems only report results on mentions. Here, we also show their performance evaluated on mention heads. When evaluated on mention heads rather than mentions13, we can always expect a performance increase for all systems on both datasets.

CompareOrContrast

acl_arc_intent_classification:train:201

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Instead, we will adopt the nomenclature of the Automatic Content Extraction program (NIST, 2004): we will call the instances of textual references to objects/abstractions mentions, which can be either named (e.g. John Mayor), nominal (the president) or pronominal (she, it). Usually, in computational linguistics literature, a named entity is an instance of a location, a person, or an organization, and the NER task consists of identifying each of these occurrences. The EDR has close ties to the named entity recognition (NER) and coreference resolution tasks, which have been the focus of several recent investigations (Bikel et al., 1997; Miller et al., 1998; Borthwick, 1999; Mikheev et al., 1999; Soon et al., 2001; Ng and Cardie, 2002; Florian et al., 2004), and have been at the center of evaluations such as: MUC-6, MUC-7, and the CoNLL'02 and CoNLL'03 shared tasks. Citation Sentence: Instead , we will adopt the nomenclature of the Automatic Content Extraction program ( NIST , 2004 ) : we will call the instances of textual references to objects/abstractions mentions , which can be either named ( e.g. John Mayor ) , nominal ( the president ) or pronominal ( she , it ) . Context after the citation: An entity is the aggregate of all the mentions (of any level) which refer to one conceptual entity. For instance, in the sentence President John Smith said he has no comments there are two mentions (named and pronomial) but only one entity, formed by the set {John Smith, he}.

Uses

acl_arc_intent_classification:train:202

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: Such a component would serve as the first stage of a clinical question answering system (Demner-Fushman and Lin, 2005) or summarization system (McKeown et al., 2003). Thus, our exploratory experiments in applying content models trained with structured RCTs on unstructured RCTs is a closer approximation of an extrinsically-valid measure of performance. The true utility of content models is to structure abstracts that have no structure to begin with. Citation Sentence: Such a component would serve as the first stage of a clinical question answering system ( Demner-Fushman and Lin , 2005 ) or summarization system ( McKeown et al. , 2003 ) . Context after the citation: We chose to focus on randomized controlled trials because they represent the standard benchmark by which all other clinical studies are measured. Table 3(b) shows the effectiveness of our trained content models on abstracts that had no explicit structure to begin with. We can see that although classification accuracy is lower than that from our cross-validation experiments, performance is quite respectable. Thus, our hypothesis that unstructured abstracts are not qualitatively different from structured abstracts appears to be mostly valid.

FutureWork

acl_arc_intent_classification:train:203

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: In the areas of Natural Language Processing (NLP) and computational linguistics, proposals have been made for using the computational Grid for data-intensive NLP and text-mining for eScience (Carroll et al., 2005; Hughes et al, 2004). In particular, representativeness and replicability are key research concerns to enhance the reliability of web data for corpora. These core foci of our work represent crucial innovations lacking in prior research. Citation Sentence: In the areas of Natural Language Processing ( NLP ) and computational linguistics , proposals have been made for using the computational Grid for data-intensive NLP and text-mining for eScience ( Carroll et al. , 2005 ; Hughes et al , 2004 ) . Context after the citation: While such an approach promises much in terms of emerging infrastructure, we wish to exploit existing computing infrastructure that is more accessible to linguists via a P2P approach. In simple terms, P2P is a technology that takes advantage of the resources and services available at the edge of the Internet (Shirky, 2001). Better known for file-sharing and Instant Messenger applications, P2P has increasingly been applied in distributed computational systems. Examples include SETI@home (looking for radio evidence of extraterrestrial life), ClimatePrediction.net (studying climate change), Predictor@home (investigating protein-related diseases) and Einstein@home (searching for gravitational signals).

Background

acl_arc_intent_classification:train:204

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Ingria (1984) comprehensively compares different approaches to complementation within grammatical theory providing a touchstone against which the LDOCE scheme can be evaluated. Michiels (1982) and Akkerman et al. (1985) provide a more detailed analysis of the information encoded by the LDOCE grammar codes and discuss their efficacy as a system of linguistic description. On the other hand, both believe and promise are assigned V3 which means they take a NP object and infinitival complement, yet there is a similar semantic distinction to be made between the two verbs; so the criteria for the assignment of the V code seem to be purely syntactic. Citation Sentence: Ingria ( 1984 ) comprehensively compares different approaches to complementation within grammatical theory providing a touchstone against which the LDOCE scheme can be evaluated . Context after the citation: Most automated parsing systems employ grammars which carefully distinguish syntactic and semantic information, therefore, if the information provided by the Longman grammar code system is to be of use, we need to be able to separate out this information and map it into a representation scheme compatible with the type of lexicon used by such parsing systems. The program which transforms the LDOCE grammar codes into lexical entries utilisable by a parser takes as input the decompacted codes and produces a relatively theory neutral representation of the lexical entry for a particular word, in the sense that this representation could be further transformed into a format suitable for most current parsing systems. For example, if the input were the third sense of believe, as in Figure 4, the program would generate the (partial) entry shown in At the time of writing, rules for producing adequate entries to drive a parsing system have only been developed for verb codes.

CompareOrContrast

acl_arc_intent_classification:train:205

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: To prove that our method is effective, we also make a comparison between the performances of our system and Xue and Palmer (2005), Xue (2008). So the ARGX sub task is improved. This made the interdependence of core arguments can be directly explored from the extraction of semantic context features. Citation Sentence: To prove that our method is effective , we also make a comparison between the performances of our system and Xue and Palmer ( 2005 ) , Xue ( 2008 ) . Context after the citation: Xue (2008) is the best SRL system until now and it has the same data setting with ours. The results are presented in Table 6. We have to point out that all the three systems are based on Gold standard parsing. From the table 6, we can find that our system is better than both of the related systems.

CompareOrContrast

acl_arc_intent_classification:train:206

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: The obtained SCFG is further used in a phrase-based and hierarchical phrase-based system (Chiang, 2007). Blunsom et al. (2008, 2009, 2010) utilized Bayesian methods to learn synchronous context free grammars (SCFG) from a parallel corpus. This work differs from the above work in that we design a novel Bayesian model to induce unsupervised U-trees, and prior knowledge can be encoded into the model more freely and effectively. Citation Sentence: The obtained SCFG is further used in a phrase-based and hierarchical phrase-based system ( Chiang , 2007 ) . Context after the citation: Levenberg et al. (2012) employed a Bayesian method to learn discontinuous SCFG rules. This study differs from their work because we concentrate on constructing tree structures for tree-based translation models. Our U-trees are learned based on STSG, which is more appropriate for tree-based translation models than SCFG. Burkett and Klein (2008) and Burkett et al. (2010) focused on joint parsing and alignment.

CompareOrContrast

acl_arc_intent_classification:train:207

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: 32 In certain cases an extension of the constraint language with named disjunctions or contexted constraints (Maxwell and Kaplan 1989; Eisele and Dorre 1990; Griffith 1996) can be used to circumvent constraint propagation. The information common to all solutions to the interaction call is lifted up into the lexical entry and becomes available upon lexical lookup. Applying constraint propagation to the extended lexical entry of Figure 17 yields the result shown in Figure 23. Citation Sentence: 32 In certain cases an extension of the constraint language with named disjunctions or contexted constraints ( Maxwell and Kaplan 1989 ; Eisele and Dorre 1990 ; Griffith 1996 ) can be used to circumvent constraint propagation . Context after the citation: Encoding the disjunctive possibilities for lexical rule application in this way, instead of with definite clause attachments, makes all relevant lexical information available at lexical lookup. For analyses proposing infinite lexica, though, a definite clause encoding of disjunctive possibilities is still necessary and constraint propagation is indispensable for efficient processing. An entry suitable for on-the-fly application (lexical rules 1 and 2 only).

Background

acl_arc_intent_classification:train:208

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Blunsom et al. (2008, 2009, 2010) utilized Bayesian methods to learn synchronous context free grammars (SCFG) from a parallel corpus. This work differs from the above work in that we design a novel Bayesian model to induce unsupervised U-trees, and prior knowledge can be encoded into the model more freely and effectively. Our previous work (Zhai et al., 2012) designed an EMbased method to construct unsupervised trees for tree-based translation models. Citation Sentence: Blunsom et al. ( 2008 , 2009 , 2010 ) utilized Bayesian methods to learn synchronous context free grammars ( SCFG ) from a parallel corpus . Context after the citation: The obtained SCFG is further used in a phrase-based and hierarchical phrase-based system (Chiang, 2007). Levenberg et al. (2012) employed a Bayesian method to learn discontinuous SCFG rules. This study differs from their work because we concentrate on constructing tree structures for tree-based translation models. Our U-trees are learned based on STSG, which is more appropriate for tree-based translation models than SCFG.

CompareOrContrast

acl_arc_intent_classification:train:209

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Tateisi et al. also translated LTAG into HPSG (Tateisi et al., 1998). All modules other than the last one are related to the conversion process from LTAG into HPSG, and the last one enables to obtain LTAG analysis from the obtained HPSG analysis. trees, and map them to LTAG derivation trees. Citation Sentence: Tateisi et al. also translated LTAG into HPSG ( Tateisi et al. , 1998 ) . Context after the citation: However, their method depended on translator’s intuitive analysis of the original grammar. Thus the translation was manual and grammar dependent. The manual translation demanded considerable efforts from the translator, and obscures the equivalence between the original and obtained grammars. Other works (Kasper et al., 1995; Becker and Lopez, 2000) convert HPSG grammars into LTAG grammars.

CompareOrContrast

acl_arc_intent_classification:train:21

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Since the arguments can provide useful semantic information, the SRL is crucial to many natural language processing tasks, such as Question and Answering (Narayanan and Harabagiu 2004), Information Extraction (Surdeanu et al. 2003), and Machine Translation(Boas 2002). Typical tags include Agent, Patient, Source, etc. and some adjuncts such as Temporal, Manner, Extent, etc. The semantic roles are marked and each of them is assigned a tag which indicates the type of the semantic relation with the related predicate. Citation Sentence: Since the arguments can provide useful semantic information , the SRL is crucial to many natural language processing tasks , such as Question and Answering ( Narayanan and Harabagiu 2004 ) , Information Extraction ( Surdeanu et al. 2003 ) , and Machine Translation ( Boas 2002 ) . Context after the citation: With the efforts of many researchers (Carreras and Màrquez 2004, 2005, Moschitti 2004, Pradhan et al 2005, Zhang et al 2007), different machine learning methods and linguistics resources are applied in this task, which has made SRL task progress fast. Compared to the research on English, the research on Chinese SRL is still in its infancy stage. Previous work on Chinese SRL mainly focused on how to transplant the machine learning methods which has been successful with English, such as Sun and Jurafsky (2004), Xue and Palmer (2005) and Xue (2008). Sun and Jurafsky (2004) did the preliminary work on Chinese SRL without any large semantically annotated corpus of Chinese.

Background

acl_arc_intent_classification:train:210

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: In addition to the model based upon a dictionary of stems and words, we also experimented with models based upon character n-grams, similar to those used for Chinese segmentation (Sproat et al., 1996). Citation Sentence: In addition to the model based upon a dictionary of stems and words , we also experimented with models based upon character n-grams , similar to those used for Chinese segmentation ( Sproat et al. , 1996 ) . Context after the citation: For these models, both arabic characters and spaces, and the inserted prefix and suffix markers appear on the arcs of the finite state machine. Here, the language model is conditioned to insert prefix and suffix markers based upon the frequency of their appearance in n-gram character contexts that appear in the training data. The character based model alone achieves a 94.5% exact match segmentation accuracy, considerably less accurate then the dictionary based model. However, an analysis of the errors indicated that the character based model is more effective at segmenting words that do not appear in the training data.

CompareOrContrast

acl_arc_intent_classification:train:211

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Notable early papers on graph-based semisupervised learning include Blum and Chawla (2001), Bansal et al. (2002), Kondor and Lafferty (2002), and Joachims (2003). inter-document references in the form of hyperlinks (Agrawal et al., 2003). Previous sentiment-analysis work in different domains has considered inter-document similarity (Agarwal and Bhattacharyya, 2005; Pang and Lee, 2005; Goldberg and Zhu, 2006) or explicit Citation Sentence: Notable early papers on graph-based semisupervised learning include Blum and Chawla ( 2001 ) , Bansal et al. ( 2002 ) , Kondor and Lafferty ( 2002 ) , and Joachims ( 2003 ) . Context after the citation: Zhu (2005) maintains a survey of this area. Recently, several alternative, often quite sophisticated approaches to collective classification have been proposed (Neville and Jensen, 2000; Lafferty et al., 2001; Getoor et al., 2002; Taskar et al., 2002; Taskar et al., 2003; Taskar et al., 2004; McCallum and Wellner, 2004). It would be interesting to investigate the application of such methods to our problem. However, we also believe that our approach has important advantages, including conceptual simplicity and the fact that it is based on an underlying optimization problem that is provably and in practice easy to solve.

Background

acl_arc_intent_classification:train:212

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: Due to using a global model like CRFs, our previous work in (Zhao et al., 2006; Zhao and Kit, 2008c) reported the best results over the evaluated corpora of Bakeoff-2 until now7. As full features are used, the former and the latter provide the similar performance. It is observed that the parsing method outperforms classification and tagging method without Markovian features or decoding throughout the whole sequence. Citation Sentence: Due to using a global model like CRFs , our previous work in ( Zhao et al. , 2006 ; Zhao and Kit , 2008c ) reported the best results over the evaluated corpora of Bakeoff-2 until now7 . Context after the citation: Though those results are slightly better than the results here, we still see that the results of character-level dependency parsing approach (Scheme E) are comparable to those state-of-the-art ones on each evaluated corpus.

CompareOrContrast

acl_arc_intent_classification:train:213

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: As already mentioned in the literature, see for example (Lapata and Lascarides, 2003), knowledge about implicit predicates could be potentially useful for a variety of NLP tasks such as language generation, information extraction, question answering or machine translation. Citation Sentence: As already mentioned in the literature , see for example ( Lapata and Lascarides , 2003 ) , knowledge about implicit predicates could be potentially useful for a variety of NLP tasks such as language generation , information extraction , question answering or machine translation . Context after the citation: Many applications of semantic relations in NLP are connected to paraphrasing or query expansion, see for example (Voorhees, 1994). Suppose that a search engine or a question answering system receives the query schnelle Bombe ’quick bomb’. Probably, in this case the user is interested in finding information about bombs that explode quickly rather then about bombs in general. Knowledge about predicates associated with the noun Bombe ’bomb’ could be used for predicting a set of probable implicit predicates.

Background

acl_arc_intent_classification:train:214

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: We use the Columbia Arabic Treebank (CATiB) (Habash and Roth 2009). Citation Sentence: We use the Columbia Arabic Treebank ( CATiB ) ( Habash and Roth 2009 ) . Context after the citation: Specifically, we use the portion converted from Part 3 of the PATB to the CATiB format, which enriches the CATiB dependency trees with full PATB morphological information. CATiB’s dependency representation is based on traditional Arabic grammar and emphasizes syntactic case relations. It has a reduced POS tag set consisting of six tags only (henceforth CATIB6). The tags are: NOM (non-proper nominals including nouns, pronouns, adjectives, and adverbs), PROP (proper nouns), VRB (active-voice verbs), VRB-PASS (passive-voice verbs), PRT (particles such as prepositions or conjunctions), and PNX (punctuation).

Uses

acl_arc_intent_classification:train:215

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: This problem may be similar to the situation in which current formal grammars allow nonsensical but parsable collections of words (e.g., "colorless green ideas... "), while before the advent of Chomskyan formalisms, a sentence was defined as the smallest meaningful collection of words; Fowler (1965, p. 546) gives 10 definitions of a sentence. Still, our definition of coherence may not be restrictive enough: two collections of sentences, one referring to "black" (about black pencils, black pullovers, and black poodles), the other one about "death" (war, cancer, etc.), connected by a sentence referring to both of these, could be interpreted as one paragraph about the new, broader topic "black + death." The question of paragraph length can probably be attended to by limiting the size of p-models, perhaps after introducing some kind of metric on logical data structures. Citation Sentence: This problem may be similar to the situation in which current formal grammars allow nonsensical but parsable collections of words ( e.g. , `` colorless green ideas ... '' ) , while before the advent of Chomskyan formalisms , a sentence was defined as the smallest meaningful collection of words ; Fowler ( 1965 , p. 546 ) gives 10 definitions of a sentence . Context after the citation: It then seems worth differentiating between the creation of a new concept like "black + death," with a meaning given by a paraphrase of the example collection of sentences, and the acceptance of the new concept—storing it in R. In our case the concept "black + death," which does not refer to any normal experiences, would be discarded as useless, although the collection of sentences would be recognized as a strange, even if coherent, paragraph. We can also hope for some fine-tuning of the notion of topic, which would prevent many offensive examples. This approach is taken in computational syntactic grammars (e.g. Jensen 1986); the number of unlikely parses is severely reduced whenever possible, but no attempt is made to define only the so-called grammatical strings of a language.

Background

acl_arc_intent_classification:train:216

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Another technique for making better use of unlabeled data is cotraining (Blum and Mitchell 1998), in which two sufficiently different learners help each other learn by labeling training data for one another. This approach assumes that there are enough existing labeled data to train the individual parsers. They show that parser performance can be improved by using boosting and bagging techniques with multiple parsers. Citation Sentence: Another technique for making better use of unlabeled data is cotraining ( Blum and Mitchell 1998 ) , in which two sufficiently different learners help each other learn by labeling training data for one another . Context after the citation: The work of Sarkar (2001) and Steedman, Osborne, et al. (2003) suggests that co-training can be helpful for statistical parsing. Pierce and Cardie (2001) have shown, in the context of base noun identification, that combining sample selection and cotraining can be an effective learning framework for large-scale training. Similar approaches are being explored for parsing (Steedman, Hwa, et al. 2003; Hwa et al. 2003).

Background

acl_arc_intent_classification:train:217

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: For example, modeling CASE in Czech improves Czech parsing (Collins et al. 1999): CASE is relevant, not redundant, and can be predicted with sufficient accuracy. In the past, it has been shown that if we can recognize the relevant morphological features in assignment configurations well enough, then they contribute to parsing accuracy. Different languages vary with respect to which features may be most helpful given various tradeoffs among these three factors. Citation Sentence: For example , modeling CASE in Czech improves Czech parsing ( Collins et al. 1999 ) : CASE is relevant , not redundant , and can be predicted with sufficient accuracy . Context after the citation: It has been more difficult showing that agreement morphology helps parsing, however, with negative results for dependency parsing in several languages (Eryigit, Nivre, and Oflazer 2008; Nivre, Boguslavsky, and Iomdin 2008; Nivre 2009). In this article we investigate morphological features for dependency parsing of Modern Standard Arabic (MSA). For MSA, the space of possible morphological features is fairly large. We determine which morphological features help and why.

Motivation

acl_arc_intent_classification:train:218

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Table look-up using an explicit translation lexicon is sufficient and preferable for many multilingual NLP applications, including "crummy" MT on the World Wide Web (Church & Hovy, 1993), certain machine-assisted translation tools (e.g. (Macklovitch, 1994; Melamed, 1996b)), concordancing for bilingual lexicography (Catizone et al., 1993; Gale & Church, 1991), computerassisted language learning, corpus linguistics (Melby. However, the IBM models, which attempt to capture a broad range of translation phenomena, are computationally expensive to apply. Over the past decade, researchers at IBM have developed a series of increasingly sophisticated statistical models for machine translation (Brown et al., 1988; Brown et al., 1990; Brown et al., 1993a). Citation Sentence: Table look-up using an explicit translation lexicon is sufficient and preferable for many multilingual NLP applications , including `` crummy '' MT on the World Wide Web ( Church & Hovy , 1993 ) , certain machine-assisted translation tools ( e.g. ( Macklovitch , 1994 ; Melamed , 1996b ) ) , concordancing for bilingual lexicography ( Catizone et al. , 1993 ; Gale & Church , 1991 ) , computerassisted language learning , corpus linguistics ( Melby . Context after the citation: 1981), and cross-lingual information retrieval (Oard & Dorr, 1996). In this paper, we present a fast method for inducing accurate translation lexicons. The method assumes that words are translated one-to-one. This assumption reduces the explanatory power of our model in comparison to the IBM models, but, as shown in Section 3.1, it helps us to avoid what we call indirect associations, a major source of errors in other models.

Background

acl_arc_intent_classification:train:219

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The system utilizes several large size biological databases including three NCBI databases (GenPept [11], RefSeq [12], and Entrez GENE [13]), PSD database from Protein Information Resources (PIR) [14], and Citation Sentence: The system utilizes several large size biological databases including three NCBI databases ( GenPept [ 11 ] , RefSeq [ 12 ] , and Entrez GENE [ 13 ] ) , PSD database from Protein Information Resources ( PIR ) [ 14 ] , and Context after the citation: Proceedings of the ACL Interactive Poster and Demonstration Sessions, pages 17–20, Ann Arbor, June 2005. c �2005 Association for Computational Linguistics UniProt [15]. Additionally, several model organism databases or nomenclature databases were used.

Uses

acl_arc_intent_classification:train:22

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Following our previous work (Karamanis and Manurung 2002; Althaus, Karamanis, and Koller 2004), the input to information ordering is an unordered set of informationbearing items represented as CF lists. Citation Sentence: Following our previous work ( Karamanis and Manurung 2002 ; Althaus , Karamanis , and Koller 2004 ) , the input to information ordering is an unordered set of informationbearing items represented as CF lists . Context after the citation: A set of candidate orderings is produced by creating different permutations of these lists. A metric of coherence uses features from centering to compute a score for each candidate ordering and select the highest scoring ordering as the output.9 A wide range of metrics of coherence can be defined in centering’s terms, simply on the basis of the work we reviewed in Section 3. To exemplify this, let us first assume that the ordering in Example (3), which is analyzed as a sequence of CF lists in Table 5, is a candidate ordering. Table 6 summarizes the NOCBs, the violations of COHERENCE, SALIENCE, and CHEAPNESS, and the centering transitions for this ordering.10 The candidate ordering contains two NOCBs in sentences (3e) and (3f).

Extends

acl_arc_intent_classification:train:220

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: There has been some controversy, at least for simple stemmers (Lovins, 1968; Porter, 1980), about the effectiveness of morphological analysis for document retrieval (Harman, 1991; Krovetz, 1993; Hull, 1996). Citation Sentence: There has been some controversy , at least for simple stemmers ( Lovins , 1968 ; Porter , 1980 ) , about the effectiveness of morphological analysis for document retrieval ( Harman , 1991 ; Krovetz , 1993 ; Hull , 1996 ) . Context after the citation: The key for quality improvement seems to be rooted mainly in the presence or absence of some form of dictionary. Empirical evidence has been brought forward that inflectional and/or derivational stemmers augmented by dictionaries indeed perform substantially better than those without access to such lexical repositories (Krovetz, 1993; Kraaij and Pohlmann, 1996; Tzoukermann et al., 1997). This result is particularly valid for natural languages with a rich morphology — both in terms of derivation and (single-word) composition. Document retrieval in these languages suffers from serious performance degradation with the stemmingonly query-term-to-text-word matching paradigm.

Background

acl_arc_intent_classification:train:221

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: This situation suggests a response-automation approach that follows the document retrieval paradigm (Salton and McGill 1983), where a new request is matched with existing response documents (e-mails). The example in Figure 1(b) illustrates a situation where specific words in the request (docking station and install) are also mentioned in the response. In our work, we focus on the first two of these situations, where either complete existing responses or parts of responses are reused to address a new request. Citation Sentence: This situation suggests a response-automation approach that follows the document retrieval paradigm ( Salton and McGill 1983 ) , where a new request is matched with existing response documents ( e-mails ) . Context after the citation: However, specific words in the request do not always match a response well, and sometimes do not match a response at all, as demonstrated by the examples in Figures 1(a) and 1(c), respectively. Sometimes requests match each other quite well, suggesting an approach where a new request is matched with an old one, and the corresponding response is reused. However, analysis of our corpus shows that this does not occur very often, because unlike response e-mails, request e-mails exhibit a high language variability: There are many customers who write these e-mails, and they differ in their background, level of expertise, and pattern of language usage. Further, there are many requests that raise multiple issues, hence matching a new request e-mail in its entirety is often not possible.

Background

acl_arc_intent_classification:train:222

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: All current approaches to monolingual TE, either syntactically oriented (Rus et al., 2005), or applying logical inference (Tatu and Moldovan, 2005), or adopting transformation-based techniques (Kouleykov and Magnini, 2005; Bar-Haim et al., 2008), incorporate different types of lexical knowledge to support textual inference. 2 Lexical resources for TE and CLTE Section 6 concludes the paper, and outlines the directions of our future research. Citation Sentence: All current approaches to monolingual TE , either syntactically oriented ( Rus et al. , 2005 ) , or applying logical inference ( Tatu and Moldovan , 2005 ) , or adopting transformation-based techniques ( Kouleykov and Magnini , 2005 ; Bar-Haim et al. , 2008 ) , incorporate different types of lexical knowledge to support textual inference . Context after the citation: Such information ranges from i) lexical paraphrases (textual equivalences between terms) to ii) lexical relations preserving entailment between words, and iii) wordlevel similarity/relatedness scores. WordNet, the most widely used resource in TE, provides all the three types of information. Synonymy relations can be used to extract lexical paraphrases indicating that words from the text and the hypothesis entail each other, thus being interchangeable. Hypernymy/hyponymy chains can provide entailmentpreserving relations between concepts, indicating that a word in the hypothesis can be replaced by a word from the text.

Background

acl_arc_intent_classification:train:223

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: This is the approach taken by IBM Models 4+ (Brown et al. 1993b; Och and Ney 2003), and more recently by the LEAF model (Fraser and Marcu 2007). One solution to this problem is to add more complexity to the model to better reflect the translation process. Word alignment models in general and the HMM in particular are very gross oversimplifications of the translation process and the optimal likelihood parameters learned often do not correspond to sensible alignments. Citation Sentence: This is the approach taken by IBM Models 4 + ( Brown et al. 1993b ; Och and Ney 2003 ) , and more recently by the LEAF model ( Fraser and Marcu 2007 ) . Context after the citation: Unfortunately, these changes make the models probabilistically deficient and intractable, requiring approximations and heuristic learning and inference prone to search errors. Instead, we propose to use a learning framework called Posterior Regularization (Grac¸a, Ganchev, and Taskar 2007) that incorporates side information into unsupervised estimation in the form of constraints on the model’s posteriors. The constraints are expressed as inequalities on the expected values under the posterior distribution of user-defined constraint features (not necessarily the same features used by the model). Because in most applications what we are interested in are the latent variables (in this case the alignments), constraining the posteriors allows a more direct way to achieve the desired behavior.

CompareOrContrast

acl_arc_intent_classification:train:224

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: We have noted that many of these desiderata make complex question answering quite similar to multi-document summarization (Lin and Demner-Fushman 2005b), but these features are also beyond the capabilities of current summarization systems. The system should collate concurrences, that is, if multiple abstracts arrive at the same conclusion—it need not be repeated unless the physician wishes to “drill down”; the system should reconcile contradictions, for example, if two abstracts disagree on a particular treatment because they studied different patient populations. Ideally, answers should integrate information from multiple clinical studies, pointing out both similarities and differences. Citation Sentence: We have noted that many of these desiderata make complex question answering quite similar to multi-document summarization ( Lin and Demner-Fushman 2005b ) , but these features are also beyond the capabilities of current summarization systems . Context after the citation: It is clear that the type of answers desired by physicians require a level of semantic analysis that is beyond the current state of the art, even with the aid of existing medical ontologies. For example, even the seemingly straightforward task of identifying similarities and differences in outcome statements is rendered exceedingly complex by the tremendous amount of background medical knowledge that must be brought to bear in interpreting clinical results and subtle differences in study design, objectives, and results; the closest analogous task in computational linguistics—redundancy detection for multi-document summarization—seems easy by comparison. Furthermore, it is unclear if textual strings make “good answers.” Perhaps a graphical rendering of the semantic predicates present in relevant abstracts might more effectively convey the desired information; see, for example, Fiszman, Rindflesch, and Kilicoglu (2004).

CompareOrContrast

acl_arc_intent_classification:train:225

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Perlmutter and Soames, 1979:472), but these are the only ones which are explicit in the LDOCE coding system. Clearly, there are other syntactic and semantic tests for this distinction, (see eg. (5) *John believed Mary that the Earth is round. Citation Sentence: Perlmutter and Soames , 1979:472 ) , but these are the only ones which are explicit in the LDOCE coding system . Context after the citation: Once the semantic type for a verb sense has been determined, the sequence of codes in the associated code field is translated, as before, on a code-by-code basis. However, when a predicate complement code is encountered, the semantic type is used to determine the type assignment, as illustrated in Figures 4 and 8 above. Where no predicate complement is involved, the letter code is usually sufficient to determine the logical properties of the verb involved. For example, T codes nearly always translate into two-place predicates as Figure 10 illustrates.

Background

acl_arc_intent_classification:train:226

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: This paper describes an approach for sharing resources in various grammar formalisms such as Feature-Based Lexicalized Tree Adjoining Grammar (FB-LTAG1) (Vijay-Shanker, 1987; Vijay-Shanker and Joshi, 1988) and Head-Driven Phrase Structure Grammar (HPSG) (Pollard and Sag, 1994) by a method of grammar conversion. Citation Sentence: This paper describes an approach for sharing resources in various grammar formalisms such as Feature-Based Lexicalized Tree Adjoining Grammar ( FB-LTAG1 ) ( Vijay-Shanker , 1987 ; Vijay-Shanker and Joshi , 1988 ) and Head-Driven Phrase Structure Grammar ( HPSG ) ( Pollard and Sag , 1994 ) by a method of grammar conversion . Context after the citation: The RenTAL system automatically converts an FB-LTAG grammar into a strongly equivalent HPSG-style grammar (Yoshinaga and Miyao, 2001). Strong equivalence means that both grammars generate exactly equivalent parse results, and that we can share the LTAG grammars and lexicons in HPSG applications. Our system can reduce considerable workload to develop a huge resource (grammars and lexicons) from scratch. Our concern is, however, not limited to the sharing of grammars and lexicons.

Background

acl_arc_intent_classification:train:227

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: These constructs correspond as directly as possible to properties of the linguistic structure that express them and are, to as small an extent as possible, dependent on the requirements of contextual resolution (unlike, say, the metavariables of standard QLFs [Alshawi and Crouch 1992], or the labels of UDRS [Reyle 1996], which are motivated entirely by the mechanisms that operate on them after grammatical processing). What is required is that QLFs are, as here, expressed in a typed higher-order logic, augmented with constructs representing the interpretation of context-dependent elements (pronouns, ellipsis, focus, etc.). But little of this detail is essential to our main aims: a wide range of grammatical formalisms and interpreters would be compatible with the basic assumptions of the contextual interpretation mechanism, assuming only that the same grammatical description is used in both the analysis and generation direction. Citation Sentence: These constructs correspond as directly as possible to properties of the linguistic structure that express them and are , to as small an extent as possible , dependent on the requirements of contextual resolution ( unlike , say , the metavariables of standard QLFs [ Alshawi and Crouch 1992 ] , or the labels of UDRS [ Reyle 1996 ] , which are motivated entirely by the mechanisms that operate on them after grammatical processing ) . Context after the citation: Syntactic properties relevant for binding constraints, parallelism, scope constraints, and so on, are not directly represented at QLF (again unlike standard QLFs) but are assumed to be available as components of the linguistic context.' The context-independent meanings of sentences, which we refer to as resolved logical forms (RLFs), are expressed in the "ordinary" subset of the QLF language. A fully resolved RLF can be directly evaluated for truth: it contains no QLF constructs. Since it is just an expression of "ordinary" logic, it could serve as a knowledge representation and reasoning language, and thus the output of some information system producing such representations could in principle feed directly into generation (modulo well-known "equivalence of logical form" problems).

Background

acl_arc_intent_classification:train:228

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Prototypes of Internet search engines for linguists, corpus linguists and lexicographers have been proposed: WebCorp (Kehoe and Renouf, 2002), KWiCFinder (Fletcher, 2004a) and the Linguist’s Search Engine (Kilgarriff, 2003; Resnik and Elkiss, 2003). Baroni and Bernardini (2004) built a corpus by iteratively searching Google for a small set of seed terms. Turney (2001) extracts word co-occurrence probabilities from unlabelled text collected from a web crawler. Citation Sentence: Prototypes of Internet search engines for linguists , corpus linguists and lexicographers have been proposed : WebCorp ( Kehoe and Renouf , 2002 ) , KWiCFinder ( Fletcher , 2004a ) and the Linguist 's Search Engine ( Kilgarriff , 2003 ; Resnik and Elkiss , 2003 ) . Context after the citation: A key concern in corpus linguistics and related disciplines is verifiability and replicability of the results of studies. Word frequency counts in internet search engines are inconsistent and unreliable (Veronis, 2005). Tools based on static corpora do not suffer from this problem, e.g. BNCweb7, developed at the University of Zurich, and View 8 (Variation in English Words and Phrases, developed at Brigham Young University) 4 http://www.comp.lancs.ac.uk/ucrel/claws/trial.html 5 http://www.comp.leeds.ac.uk/amalgam/amalgam/ amalghome.htm 6 http://www.connexor.com 7 http://homepage.mac.com/bncweb/home.html 8 http://view.byu.edu/

Background

acl_arc_intent_classification:train:229

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: The LM uses the monolingual data and is trained as a five-gram9 using the SRILM-Toolkit (Stolcke, 2002). Our Moses systems use default settings. We symmetrize using the “grow-diag-final-and” heuristic. Citation Sentence: The LM uses the monolingual data and is trained as a five-gram9 using the SRILM-Toolkit ( Stolcke , 2002 ) . Context after the citation: We run MERT separately for each system. The recaser used is the same for all systems. It is the standard recaser supplied with Moses, trained on all German training data. The dev set is wmt-2009-a and the test set is wmt-2009-b, and we report end-to-end case sensitive BLEU scores against the unmodified reference SGML file.

Uses

acl_arc_intent_classification:train:23

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Table look-up using an explicit translation lexicon is sufficient and preferable for many multilingual NLP applications, including "crummy" MT on the World Wide Web (Church & Hovy, 1993), certain machine-assisted translation tools (e.g. (Macklovitch, 1994; Melamed, 1996b)), concordancing for bilingual lexicography (Catizone et al., 1993; Gale & Church, 1991), computerassisted language learning, corpus linguistics (Melby. However, the IBM models, which attempt to capture a broad range of translation phenomena, are computationally expensive to apply. Over the past decade, researchers at IBM have developed a series of increasingly sophisticated statistical models for machine translation (Brown et al., 1988; Brown et al., 1990; Brown et al., 1993a). Citation Sentence: Table look-up using an explicit translation lexicon is sufficient and preferable for many multilingual NLP applications , including `` crummy '' MT on the World Wide Web ( Church & Hovy , 1993 ) , certain machine-assisted translation tools ( e.g. ( Macklovitch , 1994 ; Melamed , 1996b ) ) , concordancing for bilingual lexicography ( Catizone et al. , 1993 ; Gale & Church , 1991 ) , computerassisted language learning , corpus linguistics ( Melby . Context after the citation: 1981), and cross-lingual information retrieval (Oard & Dorr, 1996). In this paper, we present a fast method for inducing accurate translation lexicons. The method assumes that words are translated one-to-one. This assumption reduces the explanatory power of our model in comparison to the IBM models, but, as shown in Section 3.1, it helps us to avoid what we call indirect associations, a major source of errors in other models.

Background

acl_arc_intent_classification:train:230

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: An off-the-shelf speech recognition device, a Nippon Electric Corporation DP-200, was added to an existing natural language processing system, the Natural Language Computer (NLC) (Ballard 1979, Biermann and Ballard 1980). The usefulness of the methodology described above was tested in the implementation of a connected speech understanding system. Citation Sentence: An off-the-shelf speech recognition device , a Nippon Electric Corporation DP-200 , was added to an existing natural language processing system , the Natural Language Computer ( NLC ) ( Ballard 1979 , Biermann and Ballard 1980 ) . Context after the citation: The expectation system provided the intermediate processing between the errorful output of the speech recognizer and the deep semantics of NLC. The resulting speech understanding system is called the Voice Natural Language Computer with Expectation (VNLCE, Fink 1983). [The current system should be distinguished from an earlier voice system (VNLC, Biermann et al. 1985), which had no expectation and which handled discrete speech where a 300 millisecond pause must follow each word.] It should be emphasized, of course, that the central issue here is the study of expectation mechanisms and the details of the design decisions could have been made in rather different ways.

Background

acl_arc_intent_classification:train:231

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Some efforts have tackled tasks such as automatic image caption generation (Feng and Lapata, 2010a; Ordonez et al., 2011), text illustration (Joshi et al., 2006), or automatic location identification of Twitter users (Eisenstein et al., 2010; Wing and Baldridge, 2011; Roller et al., 2012). Others provide automatic mappings of natural language instructions to executable actions, such as interpreting navigation directions (Chen and Mooney, 2011) or robot commands (Tellex et al., 2011; Matuszek et al., 2012). Some approaches apply semantic parsing, where words and sentences are mapped to logical structure meaning (Kate and Mooney, 2007). Citation Sentence: Some efforts have tackled tasks such as automatic image caption generation ( Feng and Lapata , 2010a ; Ordonez et al. , 2011 ) , text illustration ( Joshi et al. , 2006 ) , or automatic location identification of Twitter users ( Eisenstein et al. , 2010 ; Wing and Baldridge , 2011 ; Roller et al. , 2012 ) . Context after the citation: Another line of research approaches grounded language knowledge by augmenting distributional approaches of word meaning with perceptual information (Andrews et al., 2009; Steyvers, 2010; Feng and Lapata, 2010b; Bruni et al., 2011; Silberer and Lapata, 2012; Johns and Jones, 2012; Bruni et al., 2012a; Bruni et al., 2012b; Silberer et al., 2013). Although these approaches have differed in model definition, the general goal in this line of research has been to enhance word meaning with perceptual information in order to address one of the most common criticisms of distributional semantics: that the “meaning of words is entirely given by other words” (Bruni et al., 2012b). In this paper, we explore various ways to integrate new perceptual information through novel computational modeling of this grounded knowledge into a multimodal distributional model of word meaning. The model we rely on was originally developed by

Background

acl_arc_intent_classification:train:232

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The system was trained on the Penn Treebank (Marcus et al., 1993) WSJ Sections 221 and tested on Section 23 (Table 1), same as used by Magerman (1995), Collins (1997), and Ratnaparkhi (1997), and became a common testbed. Citation Sentence: The system was trained on the Penn Treebank ( Marcus et al. , 1993 ) WSJ Sections 221 and tested on Section 23 ( Table 1 ) , same as used by Magerman ( 1995 ) , Collins ( 1997 ) , and Ratnaparkhi ( 1997 ) , and became a common testbed . Context after the citation: The tasks were selected so as to demonstrate the benefit of using internal structure data for learning composite structures. We have studied the effect of noun-phrase information on learning verb phrases by setting limits on the number of embedded instances, nemb in a tile. A limit of zero emulates the flat version since learning takes place from POS tags only.

CompareOrContrast

acl_arc_intent_classification:train:233

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Briscoe and Carroll (1997) predefine 163 verbal subcategorization frames, obtained by manually merging the classes exemplified in the COMLEX (MacLeod, Grishman, and Meyers 1994) and ANLT (Boguraev et al. 1987) dictionaries and adding around 30 frames found by manual inspection. Applying his technique to approximately four million words of New York Times newswire, Manning acquired 4,900 verb-subcategorization frame pairs for 3,104 verbs, an average of 1.6 frames per verb. The extracted frames are noisy as a result of parser errors and so are filtered using the binomial hypothesis theory (BHT), following Brent (1993). Citation Sentence: Briscoe and Carroll ( 1997 ) predefine 163 verbal subcategorization frames , obtained by manually merging the classes exemplified in the COMLEX ( MacLeod , Grishman , and Meyers 1994 ) and ANLT ( Boguraev et al. 1987 ) dictionaries and adding around 30 frames found by manual inspection . Context after the citation: The frames incorporate control information and details of specific prepositions. Briscoe and Carroll (1997) refine the BHT with a priori information about the probabilities of subcategorization frame membership and use it to filter the induced frames. Recent work by Korhonen (2002) on the filtering phase of this approach uses linguistic verb classes (based on Levin [1993]) for obtaining more accurate back-off estimates for hypothesis selection. Carroll and Rooth (1998) use a handwritten head-lexicalized, context-free grammar and a text corpus to compute the probability of particular subcategorization patterns.

Background

acl_arc_intent_classification:train:234

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Recently, several alternative, often quite sophisticated approaches to collective classification have been proposed (Neville and Jensen, 2000; Lafferty et al., 2001; Getoor et al., 2002; Taskar et al., 2002; Taskar et al., 2003; Taskar et al., 2004; McCallum and Wellner, 2004). Zhu (2005) maintains a survey of this area. Notable early papers on graph-based semisupervised learning include Blum and Chawla (2001), Bansal et al. (2002), Kondor and Lafferty (2002), and Joachims (2003). Citation Sentence: Recently , several alternative , often quite sophisticated approaches to collective classification have been proposed ( Neville and Jensen , 2000 ; Lafferty et al. , 2001 ; Getoor et al. , 2002 ; Taskar et al. , 2002 ; Taskar et al. , 2003 ; Taskar et al. , 2004 ; McCallum and Wellner , 2004 ) . Context after the citation: It would be interesting to investigate the application of such methods to our problem. However, we also believe that our approach has important advantages, including conceptual simplicity and the fact that it is based on an underlying optimization problem that is provably and in practice easy to solve.

Background

acl_arc_intent_classification:train:235

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Berstel and Reutenauer (1988) give a sufficiently general finite-state framework to allow this: weights may fall in any set K (instead of R). We will enforce an invariant: the weight of any pathset H must be (&EΠ P(π), &EΠ P(π) val(π)) E R>0 x V , from which (1) is trivial to compute. The idea is to augment the weight data structure with expectation information, so each weight records a probability and a vector counting the parameters that contributed to that probability. Citation Sentence: Berstel and Reutenauer ( 1988 ) give a sufficiently general finite-state framework to allow this : weights may fall in any set K ( instead of R ) . Context after the citation: Multiplication and addition are replaced by binary operations ® and ® on K. Thus ® is used to combine arc weights into a path weight and ® is used to combine the weights of alternative paths. To sum over infinite sets of cyclic paths we also need a closure operation *, interpreted as k* = (D'0 ki. The usual finite-state algorithms work if (K, ®, ®, *) has the structure of a closed semiring.15 Ordinary probabilities fall in the semiring (R>0, +, x, *).16 Our novel weights fall in a novel 14 Formal derivation of (1): Eπ P(ir |xi, yi) val(ir) = (Eπ P(ir, xi, yi) val(ir))/P(xi, yi) = (Eπ P(xi, yi ir)P(ir) val(ir))/ Eπ P(xi, yi |ir)P(ir); now observe that P(xi, yi |ir) = 1 or 0 according to whether ir E Π.

Uses

acl_arc_intent_classification:train:236

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: FBLTAG (Vijay-Shanker, 1987; Vijay-Shanker and Joshi, 1988) is an extension of the LTAG formalism. Adjunction grafts an auxiliary tree with the root node and foot node labeled x onto an internal node of another tree with the same symbol x (Figure 4). Substitution replaces a substitution node with another initial tree (Figure 3). Citation Sentence: FBLTAG ( Vijay-Shanker , 1987 ; Vijay-Shanker and Joshi , 1988 ) is an extension of the LTAG formalism . Context after the citation: In FB-LTAG, each node in the elementary trees has a feature structure, containing grammatical constraints on the node. Figure 5 shows a result of LTAG analysis, which is described not only by derived trees (i.e., parse trees) but also by derivation trees. A derivation tree is a structural description in LTAG and represents the history of combinations of elementary trees.

Background

acl_arc_intent_classification:train:237

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: There are several variations of such a method (Ballesteros and Croft, 1998; Pirkola, 1998; Hull 1997). This approach limits how much credit the retrieval algorithm can give to a single term in the original query and prevents the translations of one or a few terms from swamping the whole query. A second method is to structure the translated query, separating the translations for one term from translations for other terms. Citation Sentence: There are several variations of such a method ( Ballesteros and Croft , 1998 ; Pirkola , 1998 ; Hull 1997 ) . Context after the citation: One such method is to treat different translations of the same term as synonyms. Ballesteros, for example, used the INQUERY (Callan et al, 1995) synonym operator to group translations of different query terms. However, if a term has two translations in the target language, it will treat them as equal even though one of them is more likely to be the correct translation than the other. By contrast, our HMM approach supports translation probabilities.

CompareOrContrast

acl_arc_intent_classification:train:238

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: 12 In order to focus on the computational aspects of the covariation approach, in this paper we will not go into a discussion of the full lexical rule specification language introduced in Meurers (1995). The result is displayed description language. The translation of the lexical rule into a predicate is trivial. Citation Sentence: 12 In order to focus on the computational aspects of the covariation approach , in this paper we will not go into a discussion of the full lexical rule specification language introduced in Meurers ( 1995 ) . Context after the citation: The reader interested in that language and its precise interpretation can find the relevant details in that paper. 13 A more detailed presentation can be found in Minnen (in preparation). 14 We use rather abstract lexical rules in the examples to be able to focus on the relevant aspects. 15 Hinrichs and Nakazawa (1996) show that the question of whether the application criterion of lexical rules should be a subsumption or a unification test is an important question deserving of more attention.

Background

acl_arc_intent_classification:train:239

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: There has also been work focused upon determining the political leaning (e.g., “liberal” vs. “conservative”) of a document or author, where most previously-proposed methods make no direct use of relationships between the documents to be classified (the “unlabeled” texts) (Laver et al., 2003; Efron, 2004; Mullen and Malouf, 2006). Politically-oriented text Sentiment analysis has specifically been proposed as a key enabling technology in eRulemaking, allowing the automatic analysis of the opinions that people submit (Shulman et al., 2005; Cardie et al., 2006; Kwon et al., 2006). Citation Sentence: There has also been work focused upon determining the political leaning ( e.g. , `` liberal '' vs. `` conservative '' ) of a document or author , where most previously-proposed methods make no direct use of relationships between the documents to be classified ( the `` unlabeled '' texts ) ( Laver et al. , 2003 ; Efron , 2004 ; Mullen and Malouf , 2006 ) . Context after the citation: An exception is Grefenstette et al. (2004), who experimented with determining the political orientation of websites essentially by classifying the concatenation of all the documents found on that site. Others have applied the NLP technologies of near-duplicate detection and topic-based text categorization to politically oriented text (Yang and Callan, 2005; Purpura and Hillard, 2006). Detecting agreement We used a simple method to learn to identify cross-speaker references indicating agreement. More sophisticated approaches have been proposed (Hillard et al., 2003), including an extension that, in an interesting reversal of our problem, makes use of sentimentpolarity indicators within speech segments (Galley et al., 2004).

Background

acl_arc_intent_classification:train:24

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Many investigators (e.g. Allen 1976; Elowitz et al. 1976; Luce et al. 1983; Cahn 1988) have suggested that the poor prosody of synthetic speech, in comparison with natural speech, is the primary factor leading to difficulties in the comprehension of fluent synthetic speech. Existing text-to-speech systems perform well on word pronunciation and short sentences,12 but when it comes to long sentences and paragraphs, synthetic speech tends to be difficult to listen to and understand. Second, we wished to investigate how well our approach would work for determining prosodic phrasing in a text-to-speech synthesizer. Citation Sentence: Many investigators ( e.g. Allen 1976 ; Elowitz et al. 1976 ; Luce et al. 1983 ; Cahn 1988 ) have suggested that the poor prosody of synthetic speech , in comparison with natural speech , is the primary factor leading to difficulties in the comprehension of fluent synthetic speech . Context after the citation: And while researchers in text-tospeech synthesis have adopted a variety of approaches to prosodic phrase generation—from the simple punctuationbased rules and function word listings of existing commercial systems to the sophisticated prosodic heuristics described in Emorine and Martin (1988) and O'Shaughnessy (1989)--the generation of appropriate prosodic phrasing in unrestricted text has remained a problem. As we will show, our results so far indicate that our experimental system, which assigns a discourse neutral prosodic phrasing on the level of sentences, provides a significant improvement in the quality of synthesized speech. We believe that one reason for the improvement has to do with the increased pitch range that our system uses. Textto-speech systems that lack sentence-level phrasing must take a conservative approach to pitch settings in order to avoid misleading and inappropriate pitch modulations.

Motivation

acl_arc_intent_classification:train:240

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: This Principle of Finitism is also assumed by Johnson-Laird (1983), Jackendoff (1983), Kamp (1981), and implicitly or explicitly by almost all researchers in computational linguistics. The issues of control are not so important for us at this point; we restrict ourselves to describing the logic. Therefore these notions, and all other constructs we are going to define (axioms, metarules, definitions etc.) are computational, although usually we will not provide explicit algorithms for computing them. Citation Sentence: This Principle of Finitism is also assumed by Johnson-Laird ( 1983 ) , Jackendoff ( 1983 ) , Kamp ( 1981 ) , and implicitly or explicitly by almost all researchers in computational linguistics . Context after the citation: As a logical postulate it is not very radical; it is possible within a finitary framework to develop that part of mathematics that is used or has potential applications in natural science, such as mathematical analysis (cfXXX Mycielski 1981). On the other hand, a possible obstacle to our strategy of using only finite objects is the fact that the deductive closure of any set of formulas is not finite in standard logic, while, clearly, we will have to deduce new facts from formal representations of text and background knowledge. But there are several ways to avoid this obstruction. For example, consider theories consisting of universal formulas without function symbols.

CompareOrContrast

acl_arc_intent_classification:train:241

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: We performed translation experiments with an implementation of the IBM-4 translation model (Brown et al., 1993). This shows that our method was successful in producing a more focused lexicon probability distribution. Especially for (nominative) English pronouns (which have many verbs as translation candidates in the baseline lexicon), the number of translation candidates was substantially scaled down by a factor around 4. Citation Sentence: We performed translation experiments with an implementation of the IBM-4 translation model ( Brown et al. , 1993 ) . Context after the citation: A description of the system can be found in (Tillmann and Ney, 2002). Table 5 presents an assessment of translation quality for both the language pairs English—Catalan and English—Spanish. We see that there is a significant decrease in error rate for the translation into Catalan. This change is consistent across both error rates, the WER and 100—BLEU.

Uses

acl_arc_intent_classification:train:242

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: In this paper, a flexible annotation schema called Structured String-Tree Correspondence (SSTC) (Boitet & Zaharin, 1988) will be introduced to capture a natural language text, its corresponding abstract linguistic representation and the mapping (correspondence) between these two. Much of theoretical linguistics can be formulated in a very natural manner as stating correspondences (translations) between layers of representation structures (Rambow & Satta, 1996). There is now a consensus about the fact that natural language should be described as correspondences between different levels of representation. Citation Sentence: In this paper , a flexible annotation schema called Structured String-Tree Correspondence ( SSTC ) ( Boitet & Zaharin , 1988 ) will be introduced to capture a natural language text , its corresponding abstract linguistic representation and the mapping ( correspondence ) between these two . Context after the citation: The correspondence between the string and its associated representation tree structure is defined in terms of the sub-correspondence between parts of the string (substrings) and parts of the tree structure (subtrees), which can be interpreted for both analysis and generation. Such correspondence is defined in a way that is able to handle some non-standard cases (e.g. non-projective correspondence). While synchronous systems are becoming more and more popular, there is therefore a great need for formal models of corresponding different levels of representation structures. Existing synchronous systems face a problem of handling, in a computationally attractive way, some non-standard phenomena exist between NLs.

Background

acl_arc_intent_classification:train:243

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: For the development of these lists we used a collection of texts of about 300,000 words derived from the New York Times (NYT) corpus that was supplied as training data for the 7th Message Understanding Conference (MUC-7) (Chinchor 1998). These four lists can be acquired completely automatically from raw (unlabeled) texts. • frequent proper name • abbreviation (as opposed to regular word) Citation Sentence: For the development of these lists we used a collection of texts of about 300,000 words derived from the New York Times ( NYT ) corpus that was supplied as training data for the 7th Message Understanding Conference ( MUC-7 ) ( Chinchor 1998 ) . Context after the citation: We used these texts because the approach described in this article was initially designed to be part of a named-entity recognition system (Mikheev, Grover, and Moens 1998) developed for MUC-7. Although the corpus size of 300,000 words can be seen as large, the fact that this corpus does not have to be annotated in any way and that a corpus of similar size can be easily collected from on-line sources (including the Internet) makes this resource cheap to obtain. The first list on which our method relies is a list of common words. This list includes common words for a given language, but no supplementary information such as POS or morphological information is required to be present in this list.

Background

acl_arc_intent_classification:train:244

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: For the full parser, we use the one developed by Michael Collins (Collins, 1996; Collins, 1997) — one of the most accurate full parsers around. We perform our comparison using two state-ofthe-art parsers. Citation Sentence: For the full parser , we use the one developed by Michael Collins ( Collins , 1996 ; Collins , 1997 ) -- one of the most accurate full parsers around . Context after the citation: It represents a full parse tree as a set of basic phrases and a set of dependency relationships between them. Statistical learning techniques are used to compute the probabilities of these phrases and of candidate dependency relations occurring in that sentence. After that, it will choose the candidate parse tree with the highest probability as output. The experiments use the version that was trained (by Collins) on sections 02-21 of the Penn Treebank.

Uses

acl_arc_intent_classification:train:245

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: More sophisticated approaches have been proposed (Hillard et al., 2003), including an extension that, in an interesting reversal of our problem, makes use of sentimentpolarity indicators within speech segments (Galley et al., 2004). Detecting agreement We used a simple method to learn to identify cross-speaker references indicating agreement. Others have applied the NLP technologies of near-duplicate detection and topic-based text categorization to politically oriented text (Yang and Callan, 2005; Purpura and Hillard, 2006). Citation Sentence: More sophisticated approaches have been proposed ( Hillard et al. , 2003 ) , including an extension that , in an interesting reversal of our problem , makes use of sentimentpolarity indicators within speech segments ( Galley et al. , 2004 ) . Context after the citation: Also relevant is work on the general problems of dialog-act tagging (Stolcke et al., 2000), citation analysis (Lehnert et al., 1990), and computational rhetorical analysis (Marcu, 2000; Teufel and Moens, 2002). We currently do not have an efficient means to encode disagreement information as hard constraints; we plan to investigate incorporating such information in future work. Relationships between the unlabeled items Carvalho and Cohen (2005) consider sequential relations between different types of emails (e.g., between requests and satisfactions thereof) to classify messages, and thus also explicitly exploit the structure of conversations. Previous sentiment-analysis work in different domains has considered inter-document similarity (Agarwal and Bhattacharyya, 2005; Pang and Lee, 2005; Goldberg and Zhu, 2006) or explicit

Background

acl_arc_intent_classification:train:246

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Tbahriti et al. (2006) have demonstrated that differential weighting of automatically labeled sections can lead to improved retrieval performance. For example, McKnight and Srinivasan (2003) describe a machine learning approach to automatically label sentences as belonging to introduction, methods, results, or conclusion using structured abstracts as training data (see also Lin et al. 2006). The literature also contains work on sentence-level classification of MEDLINE abstracts for non-clinical purposes. Citation Sentence: Tbahriti et al. ( 2006 ) have demonstrated that differential weighting of automatically labeled sections can lead to improved retrieval performance . Context after the citation: Note, however, that such labels are orthogonal to PICO frame elements, and hence are not directly relevant to knowledge extraction for clinical question answering. In a similar vein, Light, Qiu, and Srinivasan (2004) report on the identification of speculative statements in MEDLINE abstracts, but once again, this work is not directly applicable to clinical question answering. In addition to question answering, multi-document summarization provides a complementary approach to addressing clinical information needs. The PERSIVAL project, the most comprehensive study of such techniques applied on medical texts to date, leverages patient records to generate personalized summaries in response to physicians’ queries (McKeown, Elhadad, and Hatzivassiloglou 2003; Elhadad et al. 2005).

Background

acl_arc_intent_classification:train:247

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The one-sided t-test (Hull, 1993) at significance level 0.05 indicated that the improvement on Trec5C is not statistically significant. Furthermore, the improvement on Trec5C appears to be caused by big improvements for a small number of queries. The results in Table 4 show that manual disambiguation improves performance by 17% on Trec5C, 4% on Trec4S, but not at all on Trec6C. Citation Sentence: The one-sided t-test ( Hull , 1993 ) at significance level 0.05 indicated that the improvement on Trec5C is not statistically significant . Context after the citation: It seems surprising that disambiguation does not help at all for Trec6C. We found that many terms have more than one valid translation. For example, the word "flood" (as in "flood control") has 4 valid Chinese translations. Using all of them achieves the desirable effect of query expansion.

Uses

acl_arc_intent_classification:train:248

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: mers (Lovins, 1968; Porter, 1980) demonstrably improve retrieval performance. For English, known for its limited number of inflection patterns, lexicon-free general-purpose stem1‘ ’ denotes the string concatenation operator. The efforts required for performing morphological analysis vary from language to language. Citation Sentence: mers ( Lovins , 1968 ; Porter , 1980 ) demonstrably improve retrieval performance . Context after the citation: This has been reported for other languages, too, dependent on the generality of the chosen approach (J¨appinen and Niemist¨o, 1988; Choueka, 1990; Popovic and Willett, 1992; Ekmekc¸ioglu et al., 1995; Hedlund et al., 2001; Pirkola, 2001). When it comes to a broader scope of morphological analysis, including derivation and composition, even for the English language only restricted, domain-specific algorithms exist. This is particularly true for the medical domain. From an IR view, a lot of specialized research has already been carried out for medical applications, with emphasis on the lexico-semantic aspects of dederivation and decomposition (Pacak et al., 1980; Norton and Pacak, 1983; Wolff, 1984; Wingert, 1985; Dujols et al., 1991; Baud et al., 1998).

Background

acl_arc_intent_classification:train:249

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: A number of speech understanding systems have been developed during the past fifteen years (Barnett et al. 1980, Dixon and Martin 1979, Erman et al. 1980, Haton and Pierrel 1976, Lea 1980, Lowerre and Reddy 1980, Medress 1980, Reddy 1976, Walker 1978, and Wolf and Woods 1980). Citation Sentence: A number of speech understanding systems have been developed during the past fifteen years ( Barnett et al. 1980 , Dixon and Martin 1979 , Erman et al. 1980 , Haton and Pierrel 1976 , Lea 1980 , Lowerre and Reddy 1980 , Medress 1980 , Reddy 1976 , Walker 1978 , and Wolf and Woods 1980 ) . Context after the citation: Most of these efforts concentrated on the interaction between low level information sources from a speech recognizer and a natural language processor to discover the meaning of an input sentence. While some of these systems did exhibit expectation capabilities at the sentence level, none acquired dialogues of the kind described here for the sake of dialogue level expectation and error correction. A detailed description of the kinds of expectation mechanisms appearing in these systems appears in Fink (1983). The problem of handling ill-formed input has been studied by Carbonell and Hayes (1983), Granger (1983), Jensen et al. (1983), Kwasny and Sondheimer (1981), Riesbeck and Schank (1976), Thompson (1980), Weischedel and Black (1980), and Weischedel and Sondheimer (1983).

CompareOrContrast

acl_arc_intent_classification:train:25

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: It is therefore no surprise that early attempts at response automation were knowledge-driven (Barr and Tessler 1995; Watson 1997; Delic and Lahaix 1998). circumstances can make each inquiry unique, and hence care must be taken to compose a response that does not confuse, irritate, or mislead the customer. 1 http://customercare.telephonyonline.com/ar/telecom next generation customer. Citation Sentence: It is therefore no surprise that early attempts at response automation were knowledge-driven ( Barr and Tessler 1995 ; Watson 1997 ; Delic and Lahaix 1998 ) . Context after the citation: These systems were carefully designed to produce relevant and correct responses, but required significant human input and maintenance (Delic and Lahaix 1998). In recent times, such knowledge-intensive approaches to content delivery have been largely superseded by data-intensive, statistical approaches. An outcome of the recent proliferation of statistical approaches, in particular in recommender systems and search engines, is that people have become accustomed to responses that are not precisely tailored to their queries. This indicates that help-desk customers may have also become more tolerant of inaccurate or incomplete automatically generated replies, provided these replies are still relevant to their problem, and so long as the customers can follow up with a request for human-generated responses if necessary.

Background

acl_arc_intent_classification:train:250

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The framework was originally developed for the realization of deep-syntactic structures in NLG (Lavoie and Rambow, 1997). The framework represents a generalization of several predecessor NLG systems based on Meaning-Text Theory: FoG (Kittredge and Polguere, 1991), LFS (Iordanskaja et al., 1992), and JOYCE (Rambow and Korelsky, 1992). 8 History of the Framework and Comparison with Other Systems Citation Sentence: The framework was originally developed for the realization of deep-syntactic structures in NLG ( Lavoie and Rambow , 1997 ) . Context after the citation: It was later extended for generation of deep-syntactic structures from conceptual interlingua (Kittredge and Lavoie, 1998). Finally, it was applied to MT for transfer between deep-syntactic structures of different languages (Palmer et al., 1998). The current framework encompasses the full spectrum of such transformations, i.e. from the processing of conceptual structures to the processing of deep-syntactic structures, either for NLG or MT. Compared to its predecessors (Fog, LFS, JOYCE), our approach has obvious advantages in uniformity, declarativity and portability. The framework has been used in a wider variety of domains, for more languages, and for more applications (NLG as well as MT).

Background

acl_arc_intent_classification:train:251

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: These features are very much desired in the design of an annotation scheme, in particular for the treatment of linguistic phenomena, which are non-standard, e.g. crossed dependencies (Tang & Zaharin, 1995). The SSTC is a general structure that can associate an arbitrary tree structure to string in a language as desired by the annotator to be the interpretation structure of the string, and more importantly is the facility to specify the correspondence between the string and the associated tree which can be nonprojective (Boitet & Zaharin, 1988). Citation Sentence: These features are very much desired in the design of an annotation scheme , in particular for the treatment of linguistic phenomena , which are non-standard , e.g. crossed dependencies ( Tang & Zaharin , 1995 ) . Context after the citation: Figure 2 illustrates the sentence “John picks the box up” with its corresponding SSTC. It contains a nonprojective correspondence. An interval is assigned to each word in the sentence, i.e. (0-1) for “John”, (1-2) for “picks”, (2-3) for “the", (3-4) for “box” and (4-5) for “up”. A substring in the sentence that corresponds to a node in the representation tree is denoted by assigning the interval of the substring to SNODE of 2 These definitions are based on the discussion in (Tang, 1994) and Boitet & Zaharin (1988).

Background

acl_arc_intent_classification:train:252

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Few approaches to parsing have tried to handle disfluent utterances (notable exceptions are Core & Schubert, 1999; Hindle, 1983; Nakatani & Hirschberg, 1994; Shriberg, Bear, & Dowding, 1992). This sort of processing requires quite a different architecture and different mechanisms for ambiguity resolution than one that begins processing only at the end of a complete and well-formed utterance. In humans, speech production and speech processing are done incrementally, using contextual information from the earliest moments of processing (see, e.g., Tanenhaus et al. 1995). Citation Sentence: Few approaches to parsing have tried to handle disfluent utterances ( notable exceptions are Core & Schubert , 1999 ; Hindle , 1983 ; Nakatani & Hirschberg , 1994 ; Shriberg , Bear , & Dowding , 1992 ) . Context after the citation: The few psycholinguistic experiments that have examined human processing of disfluent speech also throw into question the assumption that disfluent speech is harder to process than fluent speech. Lickley and Bard (1996) found evidence that listeners may be relatively deaf to the words in a reparandum (the part that would need to be excised in order for the utterance to be fluent), and Shriberg and Lickley (1993) found that fillers such as um or uh may be produced with a distinctive intonation that helps listeners distinguish them from the rest of the utterance. Fox Tree (1995) found that while previous restarts in an utterance may slow a listener’s monitoring for a particular word, repetitions don’t seem to hurt, and some fillers, such as uh, seem to actually speed monitoring for a subsequent word. What information exists in disfluencies, and how might speakers use it?

Background

acl_arc_intent_classification:train:253

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: 5 The open source Moses (Hoang et al. 2007) toolkit from www.statmt.org/moses/. We tested three values of the threshold (0.2, 0.4, 0.6) which try to capture different tradeoffs Symmetrization has almost no effect on alignments produced by S-HMM, but we use it for uniformity in the experiments. Citation Sentence: 5 The open source Moses ( Hoang et al. 2007 ) toolkit from www.statmt.org/moses/ . Context after the citation: 6 www.statmt.org/wmt08/baseline.html. of precision vs. recall, and pick the best according to the translation performance on development data. Table 2 summarizes the results for the different corpora. For reference we include IBM Model 4 as suggested in the task description.

Uses

acl_arc_intent_classification:train:254

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: There have been several efforts aimed at developing a domain-independent method for generating responses from a frame representation of user requests (Bobrow et al., 1977; Chu-Carroll, 1999). WIT, however, does not provide task-independent facilities that make it easier to employ such dialogue strategies. If the generation functions employ sophisticated dialogue strategies, the system can perform complicated dialogues that are not just question answering. Citation Sentence: There have been several efforts aimed at developing a domain-independent method for generating responses from a frame representation of user requests ( Bobrow et al. , 1977 ; Chu-Carroll , 1999 ) . Context after the citation: Incorporating such techniques would decrease the system developer workload. However, there has been no work on domain-independent response generation for robust spoken dialogue systems that can deal with utterances that might include pauses in the middle of a sentence, which WIT handles well. Therefore incorporating those techniques remains as a future work. Another limitation is that WIT cannot deal with multiple speech recognition candidates such as those in an N-best list.

FutureWork

acl_arc_intent_classification:train:255

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The maximum entropy approach (Berger et al., 1996) presents a powerful framework for the combination of several knowledge sources. Citation Sentence: The maximum entropy approach ( Berger et al. , 1996 ) presents a powerful framework for the combination of several knowledge sources . Context after the citation: This principle recommends to choose the distribution which preserves as much uncertainty as possible in terms of maximizing the entropy. The distribution is required to satisfy constraints, which represent facts known from the data. These constraints are expressed on the basis of feature functions hu,(s,t), where (s, t) is a pair of source and target word. The lexicon probability of a source word given the target word has the following functional form

Uses

acl_arc_intent_classification:train:256

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Other molecular biology databases We also included several model organism databases or nomenclature databases in the construction of the dictionary, i.e., mouse Mouse Genome Database (MGD) [18], fly FlyBase [19], yeast Saccharomyces Genome Database (SGD) [20], rat – Rat Genome Database (RGD) [21], worm – WormBase [22], Human Nomenclature Database (HUGO) [23], Online Mendelian Inheritance in Man (OMIM) [24], and Enzyme Nomenclature Database (ECNUM) [25, 26]. The Semantic Network contains information about the types or categories (e.g., “Disease or Syndrome”, “Virus”) to which all META concepts have been assigned. The SPECIALIST lexicon contains syntactic information for many terms, component words, and English words, including verbs, which do not appear in the META. Citation Sentence: Other molecular biology databases We also included several model organism databases or nomenclature databases in the construction of the dictionary , i.e. , mouse Mouse Genome Database ( MGD ) [ 18 ] , fly FlyBase [ 19 ] , yeast Saccharomyces Genome Database ( SGD ) [ 20 ] , rat -- Rat Genome Database ( RGD ) [ 21 ] , worm -- WormBase [ 22 ] , Human Nomenclature Database ( HUGO ) [ 23 ] , Online Mendelian Inheritance in Man ( OMIM ) [ 24 ] , and Enzyme Nomenclature Database ( ECNUM ) [ 25 , 26 ] . Context after the citation:

Uses

acl_arc_intent_classification:train:257

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: We then use the program Snob (Wallace and Boulton 1968; Wallace 2005) to cluster these experiences. We train the system by clustering the “experiences” of the response-generation methods in addressing requests, where each experience is characterized by the value of the confidence measure employed by a method and its subsequent performance, reflected by precision and recall (Equations (7) and (8), respectively). Citation Sentence: We then use the program Snob ( Wallace and Boulton 1968 ; Wallace 2005 ) to cluster these experiences . Context after the citation: Figure 8(a) is a projection of the centroids of the clusters produced by Snob into the three most significant dimensions discovered by Principal Component Analysis (PCA)—these dimensions account for 95% of the variation in the data. The bottom part of Figure 8(b) Clusters of response-generation methods obtained from the training set: (a) dimensions produced by PCA and (b) sample clusters. shows the (unprojected) centroid values of three of the clusters (the top part of the figure will be discussed subsequently).15 These clusters were chosen because they illustrate clearly three situations of interest.

Uses

acl_arc_intent_classification:train:258

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: However, studies have shown that existing systems for searching MEDLINE (such as PubMed, the search service provided by the National Library of Medicine) are often inadequate and unable to supply clinically relevant answers in a timely manner (Gorman, Ash, and Wykoff 1994; Chambliss and Conley 1996). MEDLINE, the authoritative repository of abstracts from the medical and biomedical primary literature maintained by the National Library of Medicine, provides the clinically relevant sources for answering physicians’ questions, and is commonly used in that capacity (Cogdill and Moore 1997; De Groote and Dorsch 2003). Furthermore, the need to answer questions related to patient care at the point of service has been well studied and documented (Covell, Uman, and Manning 1985; Gorman, Ash, and Wykoff 1994; Ely et al. 1999, 2005). Citation Sentence: However , studies have shown that existing systems for searching MEDLINE ( such as PubMed , the search service provided by the National Library of Medicine ) are often inadequate and unable to supply clinically relevant answers in a timely manner ( Gorman , Ash , and Wykoff 1994 ; Chambliss and Conley 1996 ) . Context after the citation: Furthermore, it is clear that traditional document retrieval technology applied to MEDLINE abstracts is insufficient for satisfactory information access; research and experience point to the need for systems that automatically analyze text and return only the relevant information, appropriately summarizing and fusing segments from multiple texts. Not only is clinical question answering interesting from a research perspective, it also represents a potentially high-impact, real-world application of language processing and information retrieval technology—better information systems to provide decision support for physicians have the potential to improve the quality of health care. Our question-answering system supports the practice of evidence-based medicine (EBM), a widely accepted paradigm for medical practice that stresses the importance of evidence from patient-centered clinical research in the health care process. EBM prescribes an approach to structuring clinical information needs and identifies elements (for example, the problem at hand and the interventions under consideration) that factor into the assessment of clinically relevant studies for medical practice.

Background

acl_arc_intent_classification:train:259

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Secondly, the cooperative principle of Grice (1975, 1978), under the assumption that referential levels of a writer and a reader are quite similar, implies that the writer should structure the text in a way that makes the construction of his intended model easy for the reader; and this seems to imply that he should appeal only to the most direct knowledge of the reader. First of all, iteration would increase the complexity of building a model of a paragraph; infinite iteration would almost certainly make impossible such a construction in real time. However, there are at least three arguments against iterating PT. Citation Sentence: Secondly , the cooperative principle of Grice ( 1975 , 1978 ) , under the assumption that referential levels of a writer and a reader are quite similar , implies that the writer should structure the text in a way that makes the construction of his intended model easy for the reader ; and this seems to imply that he should appeal only to the most direct knowledge of the reader . Context after the citation: Finally, it has been shown by Groesser (1981) that the ratio of derived to explicit information necessary for understanding a piece of text is about 8:1; furthermore, our reading of the analysis of five paragraphs by Crothers (1979) strongly suggests that only the most direct or obvious inferences are being made in the process of building a model or constructing a theory of a paragraph. Thus, for example, we can expect that in the worst case only one or two steps of such an iteration would be needed to find answers to wh-questions. Let P be a paragraph, let .15 = (s1,... , S) be its translation into a sequence of logical formulas. The set of all predicates appearing in X will be denoted by Pred(X).

Motivation

acl_arc_intent_classification:train:26

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Our implementation of the NP-based QA system uses the Empire noun phrase finder, which is described in detail in Cardie and Pierce (1998). The NP-based QA System. The filter operates on the ordered list of summary extracts for a particular question and produces a list of answer hypotheses, one for each noun phrase (NP) in the extracts in the left-to-right order in which they appeared. Citation Sentence: Our implementation of the NP-based QA system uses the Empire noun phrase finder , which is described in detail in Cardie and Pierce ( 1998 ) . Context after the citation: Empire identifies base NPs — non-recursive noun phrases — using a very simple algorithm that matches part-of-speech tag sequences based on a learned noun phrase grammar. The approach is able to achieve 94% precision and recall for base NPs derived from the Penn Treebank Wall Street Journal (Marcus et al., 1993). In the experiments below, the NP filter follows the application of the document retrieval and text summarization components. Pronoun answer hypotheses are discarded, and the NPs are assembled into 50-byte chunks.

Uses

acl_arc_intent_classification:train:260

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: For all the experiments reported in this article, we used the training portion of PATB Part 3 v3.1 (Maamouri et al. 2004), converted to the CATiB Treebank format, as mentioned in Section 2.5. Citation Sentence: For all the experiments reported in this article , we used the training portion of PATB Part 3 v3 .1 ( Maamouri et al. 2004 ) , converted to the CATiB Treebank format , as mentioned in Section 2.5 . Context after the citation: We used the same training / devtest split as in Zitouni, Sorensen, and Sarikaya (2006); and we further split the devtest into two equal parts: a development (dev) set and a blind test set. For all experiments, unless specified otherwise, we used the dev set.10 We kept the test unseen (“blind”) during training and model development. Statistics about this split (after conversion to the CATiB dependency format) are given in Table 1. For all experiments reported in this section we used the syntactic dependency parser MaltParser v1.3 (Nivre 2003, 2008; Kübler, McDonald, and Nivre 2009), a transition-based parser with an input buffer and a stack, which uses SVM classifiers

Uses

acl_arc_intent_classification:train:261

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: In addition, we consider several types of lexical features (LexF) inspired by previous work on agreement and disagreement (Galley et al., 2004; Misra and Walker, 2013). The MI approach discovers the words that are highly associated with Agree/Disagree categories and these words turn to be useful features for classification. Compared to the all unigrams baseline, the MI-based unigrams improve the F1 by 4% (Agree) and 2% (Disagree) (Table 6). Citation Sentence: In addition , we consider several types of lexical features ( LexF ) inspired by previous work on agreement and disagreement ( Galley et al. , 2004 ; Misra and Walker , 2013 ) . Context after the citation: • Sentiment Lexicon (SL): Two features are designed using a sentiment lexicon (Hu and Liu, 2004) where the first feature represents the number of times the Callout and the Target contain a positive emotional word and the second feature represents the number of the negative emotional words. • Initial unigrams in Callout (IU): Instead of using all unigrams in the Callout and Target, we only select the first words from the Callout (maximum ten). The assumption is that the stance is generally expressed at the beginning of a Callout. We used the same MI-based technique to filter any sparse words.

Motivation

acl_arc_intent_classification:train:262

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: All communicative head gestures in the videos were found and annotated with ANVIL using a subset of the attributes defined in the MUMIN annotation scheme (Allwood et al., 2007). Citation Sentence: All communicative head gestures in the videos were found and annotated with ANVIL using a subset of the attributes defined in the MUMIN annotation scheme ( Allwood et al. , 2007 ) . Context after the citation: The MUMIN scheme is a general framework for the study of gestures in interpersonal communication. In this study, we do not deal with functional classification of the gestures in themselves, but rather 1(Pa − Pe)/(1 − Pe). 2(Po − 1/c)/(1 − 1/c) where c is the number of categories. with how gestures contribute to the semantic interpretations of linguistic expressions.

Uses

acl_arc_intent_classification:train:263

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Specifically, we examine the strength of association between the verb and the noun constituent of a combination (the target expression or its lexical variants) as an indirect cue to its idiomaticity, an approach inspired by Lin (1999). We thus interpret this property statistically in the following way: We expect a lexically fixed verb+noun combination to appear much more frequently than its variants in general. This approach has two main challenges: (i) it requires prior knowledge about the idiomaticity of expressions (which is what we are developing our measure to determine); (ii) it can only measure the lexical fixedness of idiomatic combinations, and so could not apply to literal combinations. Citation Sentence: Specifically , we examine the strength of association between the verb and the noun constituent of a combination ( the target expression or its lexical variants ) as an indirect cue to its idiomaticity , an approach inspired by Lin ( 1999 ) . Context after the citation: We use the automatically built thesaurus of Lin (1998) to find words similar to each constituent, in order to automatically generate variants.2 Variants are generated by replacing either 2 We also replicated our experiments with an automatically built thesaurus created from the British National Corpus (BNC) in a similar fashion, and kindly provided to us by Diana McCarthy. Results were similar, hence we do not report them here. the noun or the verb constituent of a pair with a semantically (and syntactically) similar word.3 Examples of automatically generated variants for the pair (spill, bean) are (pour, bean), (stream, bean), (spill, corn), and (spill, rice).

Motivation

acl_arc_intent_classification:train:264

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The current system learns finite state flowcharts whereas typical learning systems usually acquire coefficient values as in Minsky and Papert (1969), assertional statements as in Michalski (1980), or semantic nets as in Winston (1975). The VNLCE processor may be considered to be a learning system of the tradition described, for example, in Michalski et al. (1984). It self activates to bias recognition toward historically observed patterns but is not otherwise observable. Citation Sentence: The current system learns finite state flowcharts whereas typical learning systems usually acquire coefficient values as in Minsky and Papert ( 1969 ) , assertional statements as in Michalski ( 1980 ) , or semantic nets as in Winston ( 1975 ) . Context after the citation: That is, the current system learns procedures rather than data structures. There is some literature on procedure acquisition such as the LISP synthesis work described in Biermann et al. (1984) and the PROLOG synthesis method of Shapiro (1982). However, the latter methodologies have not been applied to dialogue acquisition.

CompareOrContrast

acl_arc_intent_classification:train:265

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: More recently, an alignment selection approach was proposed in (Huang, 2009), which computes confidence scores for each link and prunes the links from multiple sets of alignments using a hand-picked threshold. A greedy search method was utilized and it achieved higher translation performance than the baseline. It tries to maximize the number of phrases that can be extracted in the combined alignments. Citation Sentence: More recently , an alignment selection approach was proposed in ( Huang , 2009 ) , which computes confidence scores for each link and prunes the links from multiple sets of alignments using a hand-picked threshold . Context after the citation: The alignments used in that work were generated from different aligners (HMM, block model, and maximum entropy model). In this work, we use soft voting with weighted confidence scores, where the weights can be tuned with a specific objective function. There is no need for a pre-determined threshold as used in (Huang, 2009). Also, we utilize various knowledge sources to enrich the alignments instead of using different aligners.

CompareOrContrast

acl_arc_intent_classification:train:266

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: Lin (1998) for example discusses a method where a syntactic parse of the text is performed and the context of a word is modeled using dependency triples. The current model uses the context in a very straightforward way, i.e. the two words left and right of the current word, but in the future we would like to explore more advanced methods to improve the similarity estimates. We would like to perform experiments on employing this model in other information extraction tasks, such as Word Sense Disambiguation or Named Entity Recognition. Citation Sentence: Lin ( 1998 ) for example discusses a method where a syntactic parse of the text is performed and the context of a word is modeled using dependency triples . Context after the citation: The other semi-supervised methods proposed here were less successful, although all improved on the supervised model for small training sizes. In the future we would like to improve the described automatic expansion methods, since we feel that their full potential has not yet been reached. More specifically we plan to experiment with more advanced methods to decide whether some automatically generated examples should be added to the training set.

Background

acl_arc_intent_classification:train:267

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: A variety of statistical methods were proposed over the recent years for learning to produce a full parse of free-text sentences (e.g., Bod (1992), Magerman (1995), Collins (1997), Ratnaparkhi (1997), and Sekine (1998)). Citation Sentence: A variety of statistical methods were proposed over the recent years for learning to produce a full parse of free-text sentences ( e.g. , Bod ( 1992 ) , Magerman ( 1995 ) , Collins ( 1997 ) , Ratnaparkhi ( 1997 ) , and Sekine ( 1998 ) ) . Context after the citation: In parallel, a lot of work is being done on shallow parsing (Abney, 1991; Greffenstette, 1993), focusing on partial analysis of sentences at the level of local phrases and the relations between them. Shallow parsing tasks are often formulated as dividing the sentence into nonoverlapping sequences of syntactic structures, a task called chunking. Most of the chunking works have concentrated on noun-phrases (NPs, e.g. Church (1988), Ramshaw and Marcus (1995), Cardie and Pierce (1998), Veenstra (1998)). Other chunking tasks involve recognizing subjectverb (SV) and verb-object (VO) pairs (Argamon et al., 1999; Munoz et al., 1999).

Background

acl_arc_intent_classification:train:268

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The strategies employed when MIMIC has only dialogue initiative are similar to the mixed initiative dialogue strategies employed by many existing spoken dialogue systems (e.g., (Bennacef et at., 1996; Stent et al., 1999)). rent discourse goal, we developed alternative strategies for achieving the goals in Figure 4 based on initiative distribution, as shown in Table 1. 5An alternative strategy to step (4) is to perform a database lookup based on the ambiguous query and summarize the results (Litman et al., 1998), which we leave for future work. Citation Sentence: The strategies employed when MIMIC has only dialogue initiative are similar to the mixed initiative dialogue strategies employed by many existing spoken dialogue systems ( e.g. , ( Bennacef et at. , 1996 ; Stent et al. , 1999 ) ) . Context after the citation: To instantiate an attribute, MIMIC adopts the InfoSeek dialogue act to solicit the missing information. In contrast, when MIMIC has both initiatives, it plays a more active role by presenting the user with additional information comprising valid instantiations of the attribute (GiveOptions). Given an invalid query, MIMIC notifies the user of the failed query and provides an openended prompt when it only has dialogue initiative. When MIMIC has both initiatives, however, in addition to NotifyFailure, it suggests an alternative close to the user's original query and provides a limited prompt.

CompareOrContrast

acl_arc_intent_classification:train:269

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: In modern syntactic theories (e.g., lexical-functional grammar [LFG] [Kaplan and Bresnan 1982; Bresnan 2001; Dalrymple 2001], head-driven phrase structure grammar [HPSG] [Pollard and Sag 1994], tree-adjoining grammar [TAG] [Joshi 1988], and combinatory categorial grammar [CCG] [Ades and Steedman 1982]), the lexicon is the central repository for much morphological, syntactic, and semantic information. Citation Sentence: In modern syntactic theories ( e.g. , lexical-functional grammar [ LFG ] [ Kaplan and Bresnan 1982 ; Bresnan 2001 ; Dalrymple 2001 ] , head-driven phrase structure grammar [ HPSG ] [ Pollard and Sag 1994 ] , tree-adjoining grammar [ TAG ] [ Joshi 1988 ] , and combinatory categorial grammar [ CCG ] [ Ades and Steedman 1982 ] ) , the lexicon is the central repository for much morphological , syntactic , and semantic information . Context after the citation: * National Centre for Language Technology, School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland. E-mail: {rodonovan,mburke,acahill,josef,[email protected]. † Centre for Advanced Studies, IBM, Dublin, Ireland. Submission received: 19 March 2004; revised submission received: 18 December 2004; accepted for publication: 2 March 2005.

Background

acl_arc_intent_classification:train:27

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: 7A11 our results are computed with the evalb program following the now-standard criteria in (Collins, 1999). Weight decay regularization was applied at the beginning of training but reduced to zero by the end of training. Momentum was applied throughout training. Citation Sentence: 7A11 our results are computed with the evalb program following the now-standard criteria in ( Collins , 1999 ) . Context after the citation: tical left-corner parser (Manning and Carpenter, 1997), and a PCFG (Charniak, 1997). The Tags model achieves performance which is better than any previously published results on parsing with a non-lexicalized model. The Tags model also does much better than the only other broad coverage neural network parser (Costa et al., 2001). The bottom panel of table 1 lists the results for the chosen lexicalized model (SSN-Freq>200) and five recent statistical parsers (Ratnaparkhi, 1999; Collins, 1999; Charniak, 2000; Collins, 2000; Bod, 2001).

Uses

acl_arc_intent_classification:train:270

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: We see no good reason, however, why such text spans should necessarily be sentences, since the majority of tagging paradigms (e.g., Hidden Markov Model [HMM] [Kupiec 1992], Brill’s [Brill 1995a], and MaxEnt [Ratnaparkhi 1996]) do not attempt to parse an entire sentence and operate only in the local window of two to three tokens. This requires resolving sentence boundaries before tagging. tagger operates on text spans that form a sentence. Citation Sentence: We see no good reason , however , why such text spans should necessarily be sentences , since the majority of tagging paradigms ( e.g. , Hidden Markov Model [ HMM ] [ Kupiec 1992 ] , Brill 's [ Brill 1995a ] , and MaxEnt [ Ratnaparkhi 1996 ] ) do not attempt to parse an entire sentence and operate only in the local window of two to three tokens . Context after the citation: The only reason why taggers traditionally operate on the sentence level is that a sentence naturally represents a text span in which POS information does not depend on the previous and following history. This issue can be also addressed by breaking the text into short text spans at positions where the previous tagging history does not affect current decisions. For instance, a bigram tagger operates within a window of two tokens, and thus a sequence of word tokens can be terminated at an unambiguous word token, since this unambiguous word token will be the only history used in tagging of the next token. At the same time since this token is unambiguous, it is not affected by the history.

CompareOrContrast

acl_arc_intent_classification:train:271

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: results are based on a corpus of movie subtitles (Tiedemann 2007), and are consequently shorter sentences, whereas the En→Es results are based on a corpus of parliamentary proceedings (Koehn 2005). Horizontal axis: average number of transferred edges per sentence. Vertical axis: percentage of transferred edges that are correct. Citation Sentence: results are based on a corpus of movie subtitles ( Tiedemann 2007 ) , and are consequently shorter sentences , whereas the En → Es results are based on a corpus of parliamentary proceedings ( Koehn 2005 ) . Context after the citation: We see in Figure 10 that for both domains, the models trained using posterior regularization perform better than the baseline model trained using EM.

Uses

acl_arc_intent_classification:train:272

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: The system is implemented based on (Galley et al., 2006) and (Marcu et al. 2006). The translation system used for testing the effectiveness of our U-trees is our in-house stringto-tree system (abbreviated as s2t). To create the baseline system, we use the opensource Joshua 4.0 system (Ganitkevitch et al., 2012) to build a hierarchical phrase-based (HPB) system, and a syntax-augmented MT (SAMT) 11 system (Zollmann and Venugopal, 2006) respectively. Citation Sentence: The system is implemented based on ( Galley et al. , 2006 ) and ( Marcu et al. 2006 ) . Context after the citation: In the system, we extract both the minimal GHKM rules (Galley et al., 2004), and the rules of SPMT Model 1 (Galley et al., 2006) with phrases up to length L=5 on the source side. We then obtain the composed rules by composing two or three adjacent minimal rules. To build the above s2t system, we first use the parse tree, which is generated by parsing the English side of the bilingual data with the Berkeley parser (Petrov et al., 2006). Then, we binarize the English parse trees using the head binarization approach (Wang et al., 2007) and use the resulting binary parse trees to build another s2t system.

Uses

acl_arc_intent_classification:train:273

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Also relevant is work on the general problems of dialog-act tagging (Stolcke et al., 2000), citation analysis (Lehnert et al., 1990), and computational rhetorical analysis (Marcu, 2000; Teufel and Moens, 2002). More sophisticated approaches have been proposed (Hillard et al., 2003), including an extension that, in an interesting reversal of our problem, makes use of sentimentpolarity indicators within speech segments (Galley et al., 2004). Detecting agreement We used a simple method to learn to identify cross-speaker references indicating agreement. Citation Sentence: Also relevant is work on the general problems of dialog-act tagging ( Stolcke et al. , 2000 ) , citation analysis ( Lehnert et al. , 1990 ) , and computational rhetorical analysis ( Marcu , 2000 ; Teufel and Moens , 2002 ) . Context after the citation: We currently do not have an efficient means to encode disagreement information as hard constraints; we plan to investigate incorporating such information in future work. Relationships between the unlabeled items Carvalho and Cohen (2005) consider sequential relations between different types of emails (e.g., between requests and satisfactions thereof) to classify messages, and thus also explicitly exploit the structure of conversations. Previous sentiment-analysis work in different domains has considered inter-document similarity (Agarwal and Bhattacharyya, 2005; Pang and Lee, 2005; Goldberg and Zhu, 2006) or explicit inter-document references in the form of hyperlinks (Agrawal et al., 2003).

Background

acl_arc_intent_classification:train:274

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: For projective parsing, it is significantly faster than exact dynamic programming, at the cost of small amounts of search error, We are interested in extending these ideas to phrase-structure and lattice parsing, and in trying other higher-order features, such as those used in parse reranking (Charniak and Johnson, 2005; Huang, 2008) and history-based parsing (Nivre and McDonald, 2008). Belief propagation improves non-projective dependency parsing with features that would make exact inference intractable. Citation Sentence: For projective parsing , it is significantly faster than exact dynamic programming , at the cost of small amounts of search error , We are interested in extending these ideas to phrase-structure and lattice parsing , and in trying other higher-order features , such as those used in parse reranking ( Charniak and Johnson , 2005 ; Huang , 2008 ) and history-based parsing ( Nivre and McDonald , 2008 ) . Context after the citation: We could also introduce new variables, e.g., nonterminal refinements (Matsuzaki et al., 2005), or secondary links Mid (not constrained by TREE/PTREE) that augment the parse with representations of control, binding, etc. (Sleator and Temperley, 1993; Buch-Kromann, 2006). Other parsing-like problems that could be attacked with BP appear in syntax-based machine translation. Decoding is very expensive with a synchronous grammar composed with an n-gram language model (Chiang, 2007)—but our footnote 10 suggests that BP might incorporate a language model rapidly. String alignment with synchronous grammars is quite expensive even for simple synchronous formalisms like ITG (Wu, 1997)—but Duchi et al. (2007) show how to incorporate bipartite matching into max-product BP.

FutureWork

acl_arc_intent_classification:train:275

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Word alignments are used primarily for extracting minimal translation units for machine translation (MT) (e.g., phrases [Koehn, Och, and Marcu 2003] and rules [Galley et al. 2004; Chiang et al. 2005]) as well as for Although no longer competitive as end-to-end translation models, the IBM Models, as well as the hidden Markov model (HMM) of Vogel, Ney, and Tillmann (1996), are still widely used for word alignment. The seminal work of Brown et al. (1993b) introduced a series of probabilistic models (IBM Models 1–5) for statistical machine translation and the concept of “word-byword” alignment, the correspondence between words in source and target languages. Citation Sentence: Word alignments are used primarily for extracting minimal translation units for machine translation ( MT ) ( e.g. , phrases [ Koehn , Och , and Marcu 2003 ] and rules [ Galley et al. 2004 ; Chiang et al. 2005 ] ) as well as for Context after the citation: MT system combination (Matusov, Ueffing, and Ney 2006). But their importance has grown far beyond machine translation: for instance, transferring annotations between languages (Yarowsky and Ngai 2001; Hwa et al. 2005; Ganchev, Gillenwater, and Taskar 2009); discovery of paraphrases (Bannard and Callison-Burch 2005); and joint unsupervised POS and parser induction across languages (Snyder and Barzilay 2008). IBM Models 1 and 2 and the HMM are simple and tractable probabilistic models, which produce the target sentence one target word at a time by choosing a source word and generating its translation. IBM Models 3, 4, and 5 attempt to capture fertility (the tendency of each source word to generate several target words), resulting in probabilistically deficient, intractable models that require local heuristic search and are difficult to implement and extend.

Background

acl_arc_intent_classification:train:276

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Sridhar et al. (2009) obtain promising results in dialogue act tagging of the Switchboard-DAMSL corpus using lexical, syntactic and prosodic cues, while Gravano and Hirschberg (2009) examine the relation between particular acoustic and prosodic turn-yielding cues and turn taking in a large corpus of task-oriented dialogues. Work has also been done on prosody and gestures in the specific domain of map-task dialogues, also targeted in this paper. Related are also the studies by Rieks op den Akker and Schulz (2008) and Murray and Renals (2008): both achieve promising results in the automatic segmentation of dialogue acts using the annotations in a large multimodal corpus. Citation Sentence: Sridhar et al. ( 2009 ) obtain promising results in dialogue act tagging of the Switchboard-DAMSL corpus using lexical , syntactic and prosodic cues , while Gravano and Hirschberg ( 2009 ) examine the relation between particular acoustic and prosodic turn-yielding cues and turn taking in a large corpus of task-oriented dialogues . Context after the citation: Louwerse et al. (2006) and Louwerse et al. (2007) study the relation between eye gaze, facial expression, pauses and dialogue structure in annotated English map-task dialogues (Anderson et al., 1991) and find correlations between the various modalities both within and across speakers. Finally, feedback expressions (head nods and shakes) are successfully predicted from speech, prosody and eye gaze in interaction with Embodied Communication Agents as well as human communication (Fujie et al., 2004; Morency et al., 2005; Morency et al., 2007; Morency et al., 2009). Our work is in line with these studies, all of which focus on the relation between linguistic expressions, prosody, dialogue content and gestures. In this paper, we investigate how feedback expressions can be classified into different dialogue act categories based on prosodic and gesture features.

Background

acl_arc_intent_classification:train:277

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: We have also applied our more general unification grammar acquisition methodology to the TIGER Treebank (Brants et al. 2002) and Penn Chinese Treebank (Xue, Chiou, and Palmer 2002), extracting wide-coverage, probabilistic LFG grammar In the shorter term, we intend to make the extracted subcategorization lexicons from Penn-II and Penn-III available as a downloadable public-domain research resource. The work reported here is part of the core components for bootstrapping this approach. Citation Sentence: We have also applied our more general unification grammar acquisition methodology to the TIGER Treebank ( Brants et al. 2002 ) and Penn Chinese Treebank ( Xue , Chiou , and Palmer 2002 ) , extracting wide-coverage , probabilistic LFG grammar Context after the citation: approximations and lexical resources for German (Cahill et al. 2003) and Chinese (Burke, Lam, et al. 2004). The lexical resources, however, have not yet been evaluated. This, and much else, has to await further research.

Uses

acl_arc_intent_classification:train:278

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: We use the structures previously used by Nguyen et al. (2009), and propose one new structure. Now we present the “discrete” structures followed by the kernel we used. Therefore, we use convolution kernels with a linear learning machine (Support Vector Machines) for our classification task. Citation Sentence: We use the structures previously used by Nguyen et al. ( 2009 ) , and propose one new structure . Context after the citation: Although we experimented with all of their structures,3 here we only present the ones that perform best for our classification task. All the structures and their combinations are derived from a variation of the underlying structures, Phrase Structure Trees (PST) and Dependency Trees (DT). For all trees we first extract their Path Enclosed Tree, which is the smallest common subtree that contains the two target entities (Moschitti, 2004). We use the Stanford parser (Klein and Manning, 2003) to get the basic PSTs and DTs.

Uses

acl_arc_intent_classification:train:279

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: In this paper, inspired by KNN-SVM (Zhang et al., 2006), we propose a local training method, which trains sentence-wise weights instead of a single weight, to address the above two problems. However, we can achieve this with two weights: (1, 1) for f1 and (−1, 1) for f2. Therefore, there exists no single weight W which simultaneously obtains e11 and e21 as translation for f1 and f2 via Equation (1). Citation Sentence: In this paper , inspired by KNN-SVM ( Zhang et al. , 2006 ) , we propose a local training method , which trains sentence-wise weights instead of a single weight , to address the above two problems . Context after the citation: Compared with global training methods, such as MERT, in which training and testing are separated, our method works in an online fashion, in which training is performed during testing. This online fashion has an advantage in that it can adapt the weights for each of the test sentences, by dynamically tuning the weights on translation examples which are similar to these test sentences. Similar to the method of development set automatical selection, the local training method may also suffer the problem of efficiency. To put it into practice, we propose incremental training methods which avoid retraining and iterative decoding on a development set.

Motivation

acl_arc_intent_classification:train:28

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Two exceptions to this generalisation are the Linguistic String Project (Sager, 1981) and the IBM CRITIQUE (formerly EPISTLE) Project (Heidorn et al., 1982; Byrd, 1983); the former employs a dictionary of approximately 10,000 words, most of which are specialist medical terms, the latter has well over 100,000 entries, gathered from machine readable sources. Robinson, 1982; Bobrow, 1978) consult relatively small lexicons, typically generated by hand. Few established parsing systems have substantial lexicons and even those which employ very comprehensive grammars (eg. Citation Sentence: Two exceptions to this generalisation are the Linguistic String Project ( Sager , 1981 ) and the IBM CRITIQUE ( formerly EPISTLE ) Project ( Heidorn et al. , 1982 ; Byrd , 1983 ) ; the former employs a dictionary of approximately 10,000 words , most of which are specialist medical terms , the latter has well over 100,000 entries , gathered from machine readable sources . Context after the citation: In addition, there are a number of projects under way to develop substantial lexicons from machine readable sources (see Boguraev, 1986 for details). However, as yet few results have been published concerning the utility of electronic versions of published dictionaries as sources for such lexicons. In this paper we provide an evaluation of the LDOCE grammar code system from this perspective. We chose to employ LDOCE as the machine readable source to aid the development of a substantial lexicon because this dictionary has several properties which make it uniquely appropriate for use as the core knowledge base of a natural language processing system.

CompareOrContrast

acl_arc_intent_classification:train:280

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: A number of proposals in the 1990s deliberately limited the extent to which they relied on domain and/or linguistic knowledge and reported promising results in knowledge-poor operational environments (Dagan and Itai 1990, 1991; Lappin and Leass 1994; Nasukawa 1994; Kennedy and Boguraev 1996; Williams, Harvey, and Preston 1996; Baldwin 1997; Mitkov 1996, 1998b). However, the pressing need for the development of robust and inexpensive solutions to meet the demands of practical NLP systems encouraged many researchers to move away from extensive domain and linguistic knowledge and to embark instead upon knowledge-poor anaphora resolution strategies. Much of the earlier work in anaphora resolution heavily exploited domain and linguistic knowledge (Sidner 1979; Carter 1987; Rich and LuperFoy 1988; Carbonell and Brown 1988), which was difficult both to represent and to process, and which required considerable human input. Citation Sentence: A number of proposals in the 1990s deliberately limited the extent to which they relied on domain and/or linguistic knowledge and reported promising results in knowledge-poor operational environments ( Dagan and Itai 1990 , 1991 ; Lappin and Leass 1994 ; Nasukawa 1994 ; Kennedy and Boguraev 1996 ; Williams , Harvey , and Preston 1996 ; Baldwin 1997 ; Mitkov 1996 , 1998b ) . Context after the citation: The drive toward knowledge-poor and robust approaches was further motivated by the emergence of cheaper and more reliable corpus-based NLP tools such as partof-speech taggers and shallow parsers, alongside the increasing availability of corpora and other NLP resources (e.g., ontologies). In fact, the availability of corpora, both raw and annotated with coreferential links, provided a strong impetus to anaphora resolu- tion with regard to both training and evaluation. Corpora (especially when annotated) are an invaluable source not only for empirical research but also for automated learning (e.g., machine learning) methods aiming to develop new rules and approaches; they also provide an important resource for evaluation of the implemented approaches.

Background

acl_arc_intent_classification:train:281

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Towards this aim, a flexible annotation structure called Structured String-Tree Correspondence (SSTC) was introduced in Boitet & Zaharin (1988) to record the string of terms, its associated representation structure and the mapping between the two, which is expressed by the sub-correspondences recorded as part of a SSTC. Hence, it is very much desired to define the correspondence in a way to be able to handle the non-standard cases (e.g. non-projective correspondence), see Figure 1. It is well known that many linguistic constructions are not projective (e.g. scrambling, cross serial dependencies, etc.). Citation Sentence: Towards this aim , a flexible annotation structure called Structured String-Tree Correspondence ( SSTC ) was introduced in Boitet & Zaharin ( 1988 ) to record the string of terms , its associated representation structure and the mapping between the two , which is expressed by the sub-correspondences recorded as part of a SSTC . Context after the citation: 1 The Meaning-Text Theory (MTT) was put forward in (Zolkovski & Mel'cuk (1965), in the framework of research in Machine translation. More presentations of MTT can be found in (Mel'cuk, 1997) and (Mili&evi6, 2001).

Background

acl_arc_intent_classification:train:282

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: We have built an experimental text-to-speech system that uses our analysis of prosody to generate phrase boundaries for the Olive–Liberman synthesizer (Olive and Liberman 1985). Citation Sentence: We have built an experimental text-to-speech system that uses our analysis of prosody to generate phrase boundaries for the Olive -- Liberman synthesizer ( Olive and Liberman 1985 ) . Context after the citation: Two concerns motivated our implementation. First, we hoped the system would provide us with a research tool for testing our ideas about syntax and phrasing against a large unrestricted collection of sentences. Second, we wished to investigate how well our approach would work for determining prosodic phrasing in a text-to-speech synthesizer. Existing text-to-speech systems perform well on word pronunciation and short sentences,12 but when it comes to long sentences and paragraphs, synthetic speech tends to be difficult to listen to and understand.

Uses

acl_arc_intent_classification:train:283

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: In this paper, I present a computational implementation of Distributed Morphology (Halle and Marantz, 1993), a non-lexicalist linguistic theory that erases the distinction between syntactic derivation and morphological derivation. For languages with rich derivational morphology, this problem is often critical: the standard architectural view of morphological analysis as a preprocessor presents difficulties in handling semantically meaningful affixes. Nevertheless, the lexicalist approach does not capture productive morphological processes that pervade natural language, for example, flat.V → flatten.ADJ or hammer.N → hammer.V; most frameworks for computational semantics fail to capture the deeper derivational relationship between morphologically-related terms. Citation Sentence: In this paper , I present a computational implementation of Distributed Morphology ( Halle and Marantz , 1993 ) , a non-lexicalist linguistic theory that erases the distinction between syntactic derivation and morphological derivation . Context after the citation: This framework leads to finer-grained semantics capable of better capturing linguistic generalizations.

Uses

acl_arc_intent_classification:train:284

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Some recent GRE algorithms have done away with the separation between content determination and linguistic realization, interleaving the two processes instead (Stone and Webber 1998; Krahmer and Theune 2002). Citation Sentence: Some recent GRE algorithms have done away with the separation between content determination and linguistic realization , interleaving the two processes instead ( Stone and Webber 1998 ; Krahmer and Theune 2002 ) . Context after the citation: We have separated the two phases because, in the case of vague descriptions, interleaving would tend to be difficult. Consider, for instance, the list of properties L = (size > 3 cm, size < 9 cm). If interleaving forced us to realize the two properties in L one by one, then it would no longer be possible to combine them into, for example, the largest mouse but one (if the facts in the KB support it), or even into the mice between 3 and 9 cm (since size > 3 cm is realized before size < 9 cm). Clearly, sophisticated use of gradable adjectives requires a separation between CD and linguistic realization, unless one is willing to complicate linguistic realization considerably.

CompareOrContrast

acl_arc_intent_classification:train:285

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Subsequent processing by the natural language and response generation components was done automatically by the computer (Zue et al. 1989). Instead, an experimenter in a separate room typed in the utterances as spoken by the subject. excluded. Citation Sentence: Subsequent processing by the natural language and response generation components was done automatically by the computer ( Zue et al. 1989 ) . Context after the citation: We were able to collect a total, of nearly 5000 utterances in this fashion. The speech material was then used to train the recognizer component, and the text material was used to train the natural language and back-end components. We designated a subset of 3312 sentences as the training set, and augmented the original rules so as to cover a number of sentences that appeared to stay within the domain of the back-end. We did not try to expand the rules to cover sentences that the back-end could not deal with, because we wanted to keep the natural language component tightly restricted to sentences with a likely overall success.

Uses

acl_arc_intent_classification:train:286

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: This is implemented as a cascade of simple strategies, which were briefly described in Mikheev (1999). We tackle capitalized words in a similar fashion as we tackled the abbreviations: through a document-centered approach that analyzes on the fly the distribution of ambiguously capitalized words in the entire document. Apart from being an important component in the task of text normalization, information about whether or not a capitalized word that follows a period is a common word is crucial for the SBD task, as we showed in Section 3. Citation Sentence: This is implemented as a cascade of simple strategies , which were briefly described in Mikheev ( 1999 ) . Context after the citation:

Uses

acl_arc_intent_classification:train:287

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: But, obviously, there are other possibilities—for instance, the discourse representation structures (DRS's) of Kamp (1981), which have been used to translate a subset of English into logical formulas, to model text (identified with a list of sentences), to analyze a fragment of English, and to deal with anaphora. So we will use a very simple formalism, like the one above, resembling the standard first order language. Its details are not important for our aim of giving a semantic interpretation of paragraphs; the main theses of our theory do not depend on a logical notation. Citation Sentence: But , obviously , there are other possibilities -- for instance , the discourse representation structures ( DRS 's ) of Kamp ( 1981 ) , which have been used to translate a subset of English into logical formulas , to model text ( identified with a list of sentences ) , to analyze a fragment of English , and to deal with anaphora . Context after the citation: The logical notation of Montague (1970) is more sophisticated, and may be considered another possibility. Jackendoff's (1983) formalism is richer and resembles more closely an English grammar. Jackendoff (1983, p. 14) writes &quot;it would be perverse not to take as a working assumption that language is a relatively efficient and accurate encoding of the information it conveys.&quot;

CompareOrContrast

acl_arc_intent_classification:train:288

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: Better results would be expected by combining the PCFG-LA parser with discriminative reranking approaches (Charniak and Johnson, 2005; Huang, 2008) for self training. We conjecture based on our analysis that the EM training algorithm is able to exploit the information available in both gold and automatically labeled data with more complex grammars while being less affected by over-fitting. We show for the first time that self-training is able to significantly improve the performance of a PCFG-LA parser, a single generative parser, on both small and large amounts of labeled training data. Citation Sentence: Better results would be expected by combining the PCFG-LA parser with discriminative reranking approaches ( Charniak and Johnson , 2005 ; Huang , 2008 ) for self training . Context after the citation: Self-training should also benefit other discriminatively trained parsers with latent annotations (Petrov and Klein, 2008), although training would be much slower compared to using generative models, as in our case. In future work, we plan to scale up the training process with more unlabeled training data (e.g., gigaword) and investigate automatic selection of materials that are most suitable for self-training. We also plan to investigate domain adaptation and apply the model to other languages with modest treebank resources. Finally, it is also important to explore other ways to exploit the use of unlabeled data.

FutureWork

acl_arc_intent_classification:train:289