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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: introduction Context before the citation: 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). Compared to the research on English, the research on Chinese SRL is still in its infancy stage. 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. Citation Sentence: 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 ) . Context after the citation: Sun and Jurafsky (2004) did the preliminary work on Chinese SRL without any large semantically annotated corpus of Chinese. They just labeled the predicate-argument structures of ten specified verbs to a small collection of Chinese sentences, and used Support Vector Machines to identify and classify the arguments. This paper made the first attempt on Chinese SRL and produced promising results. After the PropBank (Xue and Palmer 2003) was built, Xue and Palmer (2005) and Xue (2008) have produced more complete and systematic research on Chinese SRL.

Background

acl_arc_intent_classification:train:1358

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: Accuracy is not the best measure to assess segmentation quality, therefore we also conducted experiments using the WindowDiff measure as proposed by Pevzner and Hearst (2002). Citation Sentence: Accuracy is not the best measure to assess segmentation quality , therefore we also conducted experiments using the WindowDiff measure as proposed by Pevzner and Hearst ( 2002 ) . Context after the citation: WindowDiff returns 0 in case of a perfect segmentation; 1 is the worst possible score. However, it only takes into account segment boundaries and disregards segment types. Table 3 shows the WindowDiff scores for CCOR-ALL and CRCG-ALL. Overall, the scores are quite good and are consistently below 0.2.

Uses

acl_arc_intent_classification:train:1359

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: These translations gave rise to a number of automatically constructed linguistic resources: (1) the original (source,target) phrasal translation pairs, (2) the marker lexicon, (3) the gen11 Thanks are due to one of the anonymous reviewers for pointing out that our wEBMT system, seeded with input from multiple translation systems, with a postvalidation process via the Web (amounting to an n-gram target language model), in effect forms a multiengine MT system as described by Frederking and Nirenburg (1994), Frederking et al. (1994), and Hogan and Frederking (1998). These phrases were then translated automatically by three on-line MT systems. We have presented an EBMT system based on the marker hypothesis that uses post hoc validation and correction via the Web.11 Over 218,000 NPs and VPs were extracted automatically from the Penn-II Treebank using just 59 of its 29,000 rule types. Citation Sentence: These translations gave rise to a number of automatically constructed linguistic resources : ( 1 ) the original ( source , target ) phrasal translation pairs , ( 2 ) the marker lexicon , ( 3 ) the gen11 Thanks are due to one of the anonymous reviewers for pointing out that our wEBMT system , seeded with input from multiple translation systems , with a postvalidation process via the Web ( amounting to an n-gram target language model ) , in effect forms a multiengine MT system as described by Frederking and Nirenburg ( 1994 ) , Frederking et al. ( 1994 ) , and Hogan and Frederking ( 1998 ) . Context after the citation: eralized lexicon, and (4) the word-level lexicon. When the system is confronted with new input, these knowledge sources are searched in turn for matching chunks, and the target language chunks are combined to create translation candidates. We presented a number of experiments that showed how the system fared when confronted with NPs and sentences. For the test set of 500 NPs, we obtained translations in 96% of cases, with 77.8% of the 500 NPs being translated correctly.

CompareOrContrast

acl_arc_intent_classification:train:136

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 is in the form of an agenda-driven chart-based parser whose foundation is similar to previous formalizations of Chomsky’s Minimalist Program (Stabler, 1997; Harkema, 2000; Niyogi, 2001). My theory of verbal argument structure can be implemented in a unified morpho-syntactic parsing model that interleaves syntactic and semantic parsing. Citation Sentence: The system is in the form of an agenda-driven chart-based parser whose foundation is similar to previous formalizations of Chomsky 's Minimalist Program ( Stabler , 1997 ; Harkema , 2000 ; Niyogi , 2001 ) . Context after the citation: Lexical entries in the system are minimally specified, each consisting of a phonetic form, a list of relevant features, and semantics in the form of a A expression. The basic structure building operation, MERGE, takes two items and creates a larger item. In the process, compatible features are canceled and one of the items projects. Simultaneously, the A expression associated with the licensor is applied to the A expression associated with the licensee (in theoretical linguistic terms, SpellOut).

CompareOrContrast

acl_arc_intent_classification:train:1360

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: Optimizing for dependency arc length is particularly important as parsers tend to do worse on longer dependencies (McDonald and Nivre, 2007) and these dependencies are typically the most meaningful for downstream tasks, e.g., main verb dependencies for tasks The labeled version of this score requires that the labels of the arc are also correct. The score is normalized by the summed arc lengths for the sentence. Citation Sentence: Optimizing for dependency arc length is particularly important as parsers tend to do worse on longer dependencies ( McDonald and Nivre , 2007 ) and these dependencies are typically the most meaningful for downstream tasks , e.g. , main verb dependencies for tasks Context after the citation: 2For the graph-based parser one can also find the higest scoring tree with correct root by setting the score of all competing arcs to −∞. parsing. A parse with perfect scores under ALS and LAS will match the gold-standard training tree. However, if we were to order incorrect parses of a sentence, ALS and LAS will suggest different orderings.

Motivation

acl_arc_intent_classification:train:1361

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: Whereas Rapp & Zock (2010) dealt only with an English corpus, the current work shows that this methodology is applicable to a wide range of languages and corpora. The basic idea underlying our work is to look at foreign words, to compute their co-occurrence-based associations, and to consider these as translations of the respective words. pus, whereby the same corpus is used for several language pairs. Citation Sentence: Whereas Rapp & Zock ( 2010 ) dealt only with an English corpus , the current work shows that this methodology is applicable to a wide range of languages and corpora . Context after the citation: We were able to shed some light on criteria influencing performance, such as the selection of text type and the direction of a language pair. For example, it is more promising to look at occurrences of English words in a German corpus rather than the other way around. Because of the special status of English it is also advisable to use it as a pivot wherever possible. Perhaps surprisingly, the work may have implications regarding cognitive models of second language acquisition.

CompareOrContrast

acl_arc_intent_classification:train:1362

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: We also experiment with a CCG parser (Clark and Curran, 2007), requiring that the contexts surrounding the original phrase and paraphrase are assigned This precision-recall tradeoff reflects the inherent tradeoff between imperceptibility and payload in a Linguistic Steganography system. We find that using larger contexts leads to a high precision system (100% when using 5-grams), but at the cost of a reduced recall. Citation Sentence: We also experiment with a CCG parser ( Clark and Curran , 2007 ) , requiring that the contexts surrounding the original phrase and paraphrase are assigned Context after the citation: the same CCG lexical categories by the parser. This method increases the precision of the Google n-gram check with a slight loss in recall. A contribution of this paper is to advertise the Linguistic Steganography problem to the ACL community. The requirement that any linguistic transformation maintain the grammaticality and meaning of the cover text makes the problem a strong test for existing NLP technology.

Uses

acl_arc_intent_classification:train:1363

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: Their kernel is also very time consuming and in their more general sparse setting it requires O(mn3) time and O(mn2) space, where m and n are the number of nodes of the two trees (m >= n) (Zelenko et al., 2003). In contrast, our dependency-based word subsequence kernel, which also computes similarity between two dependency trees, is very transparent with the implicit features being simply the dependency paths. But as was also noted in (Bunescu and Mooney, 2005a), this kernel is opaque i.e. it is not obvious what the implicit features are and the authors do not describe it either. Citation Sentence: Their kernel is also very time consuming and in their more general sparse setting it requires O ( mn3 ) time and O ( mn2 ) space , where m and n are the number of nodes of the two trees ( m > = n ) ( Zelenko et al. , 2003 ) . Context after the citation: Bunescu and Mooney (2005a) give a shortest path dependency kernel for relation extraction. Their kernel, however, does not find similarity between two sentences but between the shortest dependency paths connecting the two entities of interests in the sentences. This kernel uses general dependency graphs but if the graph is a tree then the shortest path is the only path between the entities. Their kernel also uses word classes in addition to the words themselves.

FutureWork

acl_arc_intent_classification:train:1364

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:1365

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: ones, DIRT (Lin and Pantel, 2001), VerbOcean (Chklovski and Pantel, 2004), FrameNet (Baker et al., 1998), and Wikipedia (Mehdad et al., 2010; Kouylekov et al., 2009). These include, just to mention the most popular Besides WordNet, the RTE literature documents the use of a variety of lexical information sources (Bentivogli et al., 2010; Dagan et al., 2009). Citation Sentence: ones , DIRT ( Lin and Pantel , 2001 ) , VerbOcean ( Chklovski and Pantel , 2004 ) , FrameNet ( Baker et al. , 1998 ) , and Wikipedia ( Mehdad et al. , 2010 ; Kouylekov et al. , 2009 ) . Context after the citation: DIRT is a collection of statistically learned inference rules, that is often integrated as a source of lexical paraphrases and entailment rules. VerbOcean is a graph of fine-grained semantic relations between verbs, which are frequently used as a source of precise entailment rules between predicates. FrameNet is a knowledge-base of frames describing prototypical situations, and the role of the participants they involve. It can be used as an alternative source of entailment rules, or to determine the semantic overlap between texts and hypotheses.

Background

acl_arc_intent_classification:train:1366

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: include decision tree learning and Bayesian learning, nearest neighbor learning, and artificial neural networks, early such works may be found in (Lewis and Ringnette, 1994), (Creecy and Masand, 1992) and (Wiene and Pedersen, 1995), respectively. 1 Most modern Arabic writing (web, novels, articles) are written without vowels. They Citation Sentence: include decision tree learning and Bayesian learning , nearest neighbor learning , and artificial neural networks , early such works may be found in ( Lewis and Ringnette , 1994 ) , ( Creecy and Masand , 1992 ) and ( Wiene and Pedersen , 1995 ) , respectively . Context after the citation: The bulk of the text categorization work has been devoted to cope with automatic categorization of English and Latin character documents. For example, (Fang et al., 2001) discusses the evaluation of two different text categorization strategies with several variations of their feature spaces. A good study comparing document categorization algorithms can be found in (Yang and Liu, 1999). More recently, (Sebastiani, 2002) has performed a good survey of document categorization; recent works can also be found in (Joachims, 2002), (Crammer and Singer, 2003), and (Lewis et al., 2004).

Background

acl_arc_intent_classification:train:1367

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 other words AJAX is a web development technique for creating interactive web applications using a combination of XHTML and CSS, Document Object Model (or DOM), the XMLHTTPRequest object (Wikipedia, 2005). AJAX is not a technology in itself but a term that refers to the use of a group of technologies together, in particular Javascript and XML. Citation Sentence: In other words AJAX is a web development technique for creating interactive web applications using a combination of XHTML and CSS , Document Object Model ( or DOM ) , the XMLHTTPRequest object ( Wikipedia , 2005 ) . Context after the citation: AJAX paradigm has been recently defined, when someone has rediscovered a simple function originally developed by Microsoft as ActiveX control. This function, named XMLHTTPRequest lets clients ask servers for some particular data using asyncronous handshake. In this way users can continue using web application (typically filling web forms) while the client and the server exchange data and messages. Other developers have published a concurrent version of this function for other browsers than Internet Explorer, like Mozilla/Gecko, Opera and Safari.

Background

acl_arc_intent_classification:train:1368

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 these or for a specific domain, basic synonymic dictionaries can be complemented using learning methods based on distributional similarity (Pereira et al., 1993; Lin, 1998). Multi word expressions however remain a problem as they are often not or only partially included in such dictionnaries. Cases of intracategorial synonymy are relatively straigthtforward as several electronic synonym dictionnaries for french are available (Ploux, 1997). Citation Sentence: For these or for a specific domain , basic synonymic dictionaries can be complemented using learning methods based on distributional similarity ( Pereira et al. , 1993 ; Lin , 1998 ) . Context after the citation: techniques. For intercategorial synonymy involving a derivational morphology link, some resources are available which however are only partial in that they only store morphological families that is, sets of items that are morphologically related. Lexical semantics information still need to be included. Intercategorial synonymy not involving a derivational morphology link has been little studied and resources are lacking.

FutureWork

acl_arc_intent_classification:train:1369

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 includes work on generalized expectation (Mann and McCallum, 2010), posterior regularization (Ganchev et al., 2010) and constraint driven learning (Chang et al., 2007; Chang et al., 2010). There have been a number of efforts to exploit weak or external signals of quality to train better prediction models. We call our algorithm augmented-loss training as it optimizes multiple losses to augment the traditional supervised parser loss. Citation Sentence: This includes work on generalized expectation ( Mann and McCallum , 2010 ) , posterior regularization ( Ganchev et al. , 2010 ) and constraint driven learning ( Chang et al. , 2007 ; Chang et al. , 2010 ) . Context after the citation: The work of Chang et al. (2007) on constraint driven learning is perhaps the closest to our framework and we draw connections to it in Section 5. In these studies the typical goal is to use the weak signal to improve the structured prediction models on the intrinsic evaluation metrics. For our setting this would mean using weak application specific signals to improve dependency parsing. Though we explore such ideas in our experiments, in particular for semi-supervised domain adaptation, we are primarily interested in the case where the weak signal is precisely what we wish to optimize, but also desire the benefit from using both data with annotated parse structures and data specific to the task at hand to guide parser training.

Background

acl_arc_intent_classification:train:137

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: converted to numerical features using the standard technique of binarization, and we split values of the FEATS field into its atomic components.4 For some languages, we divide the training data into smaller sets, based on some feature s (normally the CPOS or POS of the next input token), which may reduce training times without a significant loss in accuracy (Yamada and Matsumoto, 2003). 3We also ran preliminary experiments with memory-based learning but found that this gave consistently lower accuracy. 2The fields PHEAD and PDEPREL have not been used at all, since we rely on pseudo-projective parsing for the treatment of non-projective structures. Citation Sentence: converted to numerical features using the standard technique of binarization , and we split values of the FEATS field into its atomic components .4 For some languages , we divide the training data into smaller sets , based on some feature s ( normally the CPOS or POS of the next input token ) , which may reduce training times without a significant loss in accuracy ( Yamada and Matsumoto , 2003 ) . Context after the citation: To avoid too small training sets, we pool together categories that have a frequency below a certain threshold t.

Background

acl_arc_intent_classification:train:1370

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: • language learning (Green 1979; Mori and Moeser 1983; Morgan, Meier, and Newport 1989) • monolingual grammar induction (Juola 1998) • grammar optimization (Juola 1994) • insights into universal grammar (Juola 1998) • machine translation (Juola 1994, 1997; Veale and Way 1997; Gough, Way, and Hearne 2002) The marker hypothesis has been used for a number of different language-related tasks, including The marker hypothesis is arguably universal in presuming that concepts and structures like these have similar morphological or structural marking in all languages. Citation Sentence: • language learning ( Green 1979 ; Mori and Moeser 1983 ; Morgan , Meier , and Newport 1989 ) • monolingual grammar induction ( Juola 1998 ) • grammar optimization ( Juola 1994 ) • insights into universal grammar ( Juola 1998 ) • machine translation ( Juola 1994 , 1997 ; Veale and Way 1997 ; Gough , Way , and Hearne 2002 ) Context after the citation: With respect to translation, a potential problem in using the marker hypothesis is that some languages do not have marker words such as articles, for instance. Green’s (1979) work showed that artificial languages, both with and without specific marker words, may be learned more accurately and quickly if such psycholinguistic cues exist. The research of Mori and Moeser (1983) showed a similar effect due to case marking on pseudowords in such artificial languages, and Morgan, Meier, and Newport (1989) demonstrated that languages that do not permit pronouns as substitutes for phrases also provide evidence in favor of the marker hypothesis.

Background

acl_arc_intent_classification:train:1371

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: Linguistic preprocessing of text documents is carried out by re-using smes, an information extraction core system for real-world German text processing (Neumann et al., 1997). Citation Sentence: Linguistic preprocessing of text documents is carried out by re-using smes , an information extraction core system for real-world German text processing ( Neumann et al. , 1997 ) . Context after the citation: The fundamental design criterion of smes is to provide a set of basic, powerful, robust, and efficient STP components and 4Almost all tools we examined build a single multicategorizer except for SVM_Light, which builds multiple binary classifiers. (categorized Data generic linguistic knowledge sources that can easily be customized to deal with different tasks in a flexible manner. smes includes a text tokenizer, a lexical processor and a chunk parser.

Uses

acl_arc_intent_classification:train:1372

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: Regarding future work, there are many research line that may be followed: i) Capturing more features by employing external knowledge such as ontological, lexical resource or WordNet-based features (Basili et al., 2005a; Basili et al., 2005b; Bloehdorn et al., 2006; Bloehdorn and Moschitti, 2007) or shallow semantic trees, (Giuglea and Moschitti, 2004; Giuglea and Moschitti, 2006; Moschitti and Bejan, 2004; Moschitti et al., 2007; Moschitti, 2008; Moschitti et al., 2008). Our novel composite kernels, which account for the two syntactic structures, are experimented with the appropriate convolution kernels and show significant improvement with respect to the state-ofthe-art in RE. For the design of automatic relation classifiers, we have investigated the impact of dependency structures to the RE task. Citation Sentence: Regarding future work , there are many research line that may be followed : i ) Capturing more features by employing external knowledge such as ontological , lexical resource or WordNet-based features ( Basili et al. , 2005a ; Basili et al. , 2005b ; Bloehdorn et al. , 2006 ; Bloehdorn and Moschitti , 2007 ) or shallow semantic trees , ( Giuglea and Moschitti , 2004 ; Giuglea and Moschitti , 2006 ; Moschitti and Bejan , 2004 ; Moschitti et al. , 2007 ; Moschitti , 2008 ; Moschitti et al. , 2008 ) . Context after the citation: ii) Design a new tree-based structures, which combines the information of both constituent and dependency parses. From dependency trees we can extract more precise but also more sparse relationships (which may cause overfit). From constituent trees, we can extract subtrees constituted by non-terminal symbols (grammar symbols), which provide a better generalization (with a risk of underfitting). iii) Design a new kernel which can integrate the advantages of the constituent and dependency tree.

FutureWork

acl_arc_intent_classification:train:1373

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 English −> Urdu, Juola (1997, page 213) notes that “the system learned the original training corpus ... perfectly and could reproduce it without errors”; that is, it scored 100% accuracy when tested against the training corpus. For the English −> French language pair, Juola gives results of 61% correct translation when the system is tested on the training corpus, and 36% accuracy when it is evaluated with test data. Juola (1994, 1997) conducts some small experiments using his METLA system to show the viability of this approach for English −> French and English −> Urdu. Citation Sentence: For English − > Urdu , Juola ( 1997 , page 213 ) notes that `` the system learned the original training corpus ... perfectly and could reproduce it without errors '' ; that is , it scored 100 % accuracy when tested against the training corpus . Context after the citation: On novel test sentences, he gives results of 72% correct translation. In their Gaijin system, Veale and Way (1997) give a result of 63% accurate translations obtained for English −> German on a test set of 791 sentences from CorelDRAW manuals. As in METLA and Gaijin, we exploit lists of known marker words for each language to indicate the start and end of segments. For English, our source language, we use the sets of marker words in (13): <DET> {the, a, an, those, these, ... } <PREP> {in, on, out, with, from, to, under, ... } <QUANT> {all, some, few, many, ... }

Background

acl_arc_intent_classification:train:1374

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: Much previous work looks at the impact of using source side information (i.e., feature functions on the aligned English), such as those of Avramidis and Koehn (2008), Yeniterzi and Oflazer (2010) and others. So it is questionable as to whether morpheme segmentation techniques are sufficient to solve the inflectional problem we are addressing. In German these linguistic features are marked very irregularly and there is widespread syncretism, making it difficult to split off morphemes specifying these features. Citation Sentence: Much previous work looks at the impact of using source side information ( i.e. , feature functions on the aligned English ) , such as those of Avramidis and Koehn ( 2008 ) , Yeniterzi and Oflazer ( 2010 ) and others . Context after the citation: Toutanova et. al.’s work showed that it is most important to model target side coherence and our stem markup also allows us to access source side information. Using additional source side information beyond the markup did not produce a gain in performance. For compound splitting, we follow Fritzinger and Fraser (2010), using linguistic knowledge encoded in a rule-based morphological analyser and then selecting the best analysis based on the geometric mean of word part frequencies.

CompareOrContrast

acl_arc_intent_classification:train:1375

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: Recent work (Banko and Brill, 2001; Curran and Moens, 2002) has suggested that some tasks will benefit from using significantly more data. However, the greatest increase is in the amount of raw text available to be processed, e.g. the English Gigaword Corpus (Linguistic Data Consortium, 2003). This will require more efficient learning algorithms and implementations. Citation Sentence: Recent work ( Banko and Brill , 2001 ; Curran and Moens , 2002 ) has suggested that some tasks will benefit from using significantly more data . Context after the citation: Also, many potential applications of NLP will involve processing very large text databases. For instance, biomedical text-mining involves extracting information from the vast body of biological and medical literature; and search engines may eventually apply NLP techniques to the whole web. Other potential applications must process text online or in realtime. For example, Google currently answers 250 million queries per day, thus processing time must be minimised.

Background

acl_arc_intent_classification:train:1376

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: See Nederhof (1998) for a variant of this approximation that constructs finite transducers rather than finite automata. However, our implementation applies the construction separately for each nonterminal in a set N, such that recursive(N) = self, which leads to a separate subautomaton of the compact representation (Section 3). For the sake of presentational convenience, the above describes a construction working on the complete grammar. Citation Sentence: See Nederhof ( 1998 ) for a variant of this approximation that constructs finite transducers rather than finite automata . Context after the citation: We have further implemented a parameterized version of the RTN approximation. A state of the nondeterministic automaton is now also associated to a list H of length IHI strictly smaller than a number d, which is the parameter to the method. This list represents a history of rule positions that were encountered in the computation leading to the present state. More precisely, we define an item to be an object of the form [A —> a • 0], where A —> ai3 is a rule from the grammar.

Background

acl_arc_intent_classification:train:1377

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: Due to their remarkable ability to incorporate context structure information and long distance reordering into the translation process, tree-based translation models have shown promising progress in improving translation quality (Liu et al., 2006, 2009; Quirk et al., 2005; Galley et al., 2004, 2006; Marcu et al., 2006; Shen et al., 2008; Zhang et al., 2011b). In recent years, tree-based translation models1 are drawing more and more attention in the community of statistical machine translation (SMT). Citation Sentence: Due to their remarkable ability to incorporate context structure information and long distance reordering into the translation process , tree-based translation models have shown promising progress in improving translation quality ( Liu et al. , 2006 , 2009 ; Quirk et al. , 2005 ; Galley et al. , 2004 , 2006 ; Marcu et al. , 2006 ; Shen et al. , 2008 ; Zhang et al. , 2011b ) . Context after the citation: However, tree-based translation models always suffer from two major challenges: 1) They are usually built directly from parse trees, which are generated by supervised linguistic parsers. 1 A tree-based translation model is defined as a model using tree structures on one side or both sides. However, for many language pairs, it is difficult to acquire such corresponding linguistic parsers due to the lack of Tree-bank resources for training. 2) Parse trees are actually only used to model and explain the monolingual structure, rather than the bilingual mapping between language pairs.

Background

acl_arc_intent_classification:train:1378

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 include devices such as interleaving the components (McDonald 1983; Appelt 1983), backtracking on failure (Appelt 1985; Nogier 1989), allowing the linguistic component to interrogate the planner (Mann 1983; Sondheimer and Nebel 1986), and Hovy's notion of restrictive (i.e., bottom-up) planning (Hovy 1988a, 1988c). There have in fact been attempts to develop modified modular designs that allow generators to handle interactions between the components. Certainly an approach to generation that does handle these interactions would be an improvement, as long as it didn't require abandoning modularity. Citation Sentence: These include devices such as interleaving the components ( McDonald 1983 ; Appelt 1983 ) , backtracking on failure ( Appelt 1985 ; Nogier 1989 ) , allowing the linguistic component to interrogate the planner ( Mann 1983 ; Sondheimer and Nebel 1986 ) , and Hovy 's notion of restrictive ( i.e. , bottom-up ) planning ( Hovy 1988a , 1988c ) . Context after the citation: All of these approaches, though, require that potential interactions be determined either by the tactical component or by the system designer in advance. The text planning component still has no way to detect and respond to unanticipated interactions on its own initiative.5 4 Danlos still has a separate low-level &quot;syntactic&quot; component, but essentially all of the generator's decisions are made by the strategic component. 5 In fact, adding additional components may make the problem even worse, as decisions may then be spread across three or more separate components.

Background

acl_arc_intent_classification:train:1379

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: The list, a synthesis of a number of relation lists cited in the literature, has been designed to be general, domainindependent (Barker et al., 1997a). We have a list of 47 relations that manifest themselves in compound clauses, inside a simple clause or in noun phrases. If such a graph is found, then the system maps previously assigned semantic relations onto the current syntactic graph. Citation Sentence: The list , a synthesis of a number of relation lists cited in the literature , has been designed to be general , domainindependent ( Barker et al. , 1997a ) . Context after the citation: Section 2 overviews research in semantic relation analysis. Section 3 describes the text we used in ex- Workshop on TextGraphs, at HLT-NAACL 2006, pages 81–88, New York City, June 2006. c �2006 Association for Computational Linguistics periments, and the semantic relation list.

Motivation

acl_arc_intent_classification:train:138

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 shallow parser used is the SNoW-based CSCL parser (Punyakanok and Roth, 2001; Munoz et al., 1999). The reported results for the full parse tree (on section 23) are recall/precision of 88.1/87.5 (Collins, 1997). The experiments use the version that was trained (by Collins) on sections 02-21 of the Penn Treebank. Citation Sentence: The shallow parser used is the SNoW-based CSCL parser ( Punyakanok and Roth , 2001 ; Munoz et al. , 1999 ) . Context after the citation: SNoW (Carleson et al., 1999; Roth, 1998) is a multi-class classifier that is specifically tailored for learning in domains in which the potential number of information sources (features) taking part in decisions is very large, of which NLP is a principal example. It works by learning a sparse network of linear functions over a pre-defined or incrementally learned feature space. Typically, SNoW is used as a classifier, and predicts using a winner-take-all mechanism over the activation value of the target classes. However, in addition to the prediction, it provides a reliable confidence level in the prediction, which enables its use in an inference algorithm that combines predictors to produce a coherent inference.

Uses

acl_arc_intent_classification:train:1380

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: Other approaches use less deep linguistic resources (e.g., POS-tags Stymne (2008)) or are (almost) knowledge-free (e.g., Koehn and Knight (2003)). For compound splitting, we follow Fritzinger and Fraser (2010), using linguistic knowledge encoded in a rule-based morphological analyser and then selecting the best analysis based on the geometric mean of word part frequencies. Using additional source side information beyond the markup did not produce a gain in performance. Citation Sentence: Other approaches use less deep linguistic resources ( e.g. , POS-tags Stymne ( 2008 ) ) or are ( almost ) knowledge-free ( e.g. , Koehn and Knight ( 2003 ) ) . Context after the citation: Compound merging is less well studied. Popovic et al. (2006) used a simple, list-based merging approach, merging all consecutive words included in a merging list. This approach resulted in too many compounds. We follow Stymne and Cancedda (2011), for compound merging.

CompareOrContrast

acl_arc_intent_classification:train:1381

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 well-known approaches include rule-based models (Brill and Resnik 1994), backed-off models (Collins and Brooks 1995), and a maximumentropy model (Ratnaparkhi 1998). Researchers have proposed many computational models for resolving PPattachment ambiguities. One common source of structural ambiguities arises from syntactic constructs in which a prepositional phrase might be equally likely to modify the verb or the noun preceding it. Citation Sentence: Some well-known approaches include rule-based models ( Brill and Resnik 1994 ) , backed-off models ( Collins and Brooks 1995 ) , and a maximumentropy model ( Ratnaparkhi 1998 ) . Context after the citation: Following the tradition of using learning PPattachment as a way to gain insight into the parsing problem, we first apply sample selection to reduce the amount of annotation used in training a PP-attachment model. We use the Collins-Brooks model as the basic learning algorithm and experiment with several evaluation functions based on the types of predictive criteria described earlier. Our experiments show that the best evaluation function can reduce the number of labeled examples by nearly half without loss of accuracy.

Background

acl_arc_intent_classification:train:1382

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: One approach to partial parsing was presented by Buchholz et al. (1999), who extended a shallow-parsing technique to partial parsing. It is an extension of shallow parsing towards handling composite and multiple patterns, while maintaining the local nature of the task, and simplicity of training material. The approach presented here, of trainable partial parsing, attempts to reduce the gap between shallow and full parsing. Citation Sentence: One approach to partial parsing was presented by Buchholz et al. ( 1999 ) , who extended a shallow-parsing technique to partial parsing . Context after the citation: The output of NP and VP chunking was used as an input to grammatical relation inference. The inferences process is cascaded, and a clear improvement was obtained by passing results across cascades. Another approach for partial parsing was presented by Skut and Brants (1998). Their method is an extension of that of Church (1988) for finding NP's, achieved by extending the feature space to include structural information.

Background

acl_arc_intent_classification:train:1383

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: Soricut and Brill (2006) compared a predictive approach (statistical translation), a retrieval approach based on a language-model, and a hybrid approach which combines statistical chunking and traditional retrieval. Jijkoun and de Rijke (2005) compared different variants of retrieval techniques. Berger et al. (2000) compared two retrieval approaches (TF.IDF and query expansion) and two predictive approaches (statistical translation and latent variable models). Citation Sentence: Soricut and Brill ( 2006 ) compared a predictive approach ( statistical translation ) , a retrieval approach based on a language-model , and a hybrid approach which combines statistical chunking and traditional retrieval . Context after the citation: Two significant differences between help-desk and FAQs are the following. • The responses in the help-desk corpus are personalized, which means that on one hand, we must abstract from them sufficiently to obtain meaningful regularities, and on the other hand, we must be careful not to abstract away specific information that addresses particular issues. • Help-desk responses have much more repetition than FAQs, because the corpus is made up of individual dialogues, rather than typical request–response pairs. This motivates the use of multi-document summarization techniques, rather than question-answering approaches, to extract individual answers.

CompareOrContrast

acl_arc_intent_classification:train:1384

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: Indeed, such rich semantic links can be used to extend indices or reformulate queries (similar to the work by Voorhees (1994) with WoRDNET relations). Even though this work is carried out for terminographical and lexicographical purposes, it can certainly be of use in other applications, namely information retrieval. The work is carried out in order to assist terminographers in the enrichment of a dictionary on computing that includes collocational information (L'Homme, 2004). Citation Sentence: Indeed , such rich semantic links can be used to extend indices or reformulate queries ( similar to the work by Voorhees ( 1994 ) with WoRDNET relations ) . Context after the citation:

CompareOrContrast

acl_arc_intent_classification:train:1385

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:1386

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: Despite these arguments, most applied NLG systems use a pipelined architecture; indeed, a pipeline was used in every one of the systems surveyed by Reiter (1994) and Paiva (1998). This is largely due to the fact that pipelined systems cannot perform general search over a decision space that includes decisions made in more than one module. The common theme behind many of these examples is that pipelines have difficulties satisfying linguistic constraints (such as unambiguous reference) or performing linguistic optimizations (such as using pronouns instead of longer referring expressions whenever possible) in cases where the constraints or optimizations depend on decisions made in multiple modules. Citation Sentence: Despite these arguments , most applied NLG systems use a pipelined architecture ; indeed , a pipeline was used in every one of the systems surveyed by Reiter ( 1994 ) and Paiva ( 1998 ) . Context after the citation: This may be because pipelines have many engineering advantages, and in practice the sort of problems pointed out by Danlos and other pipeline critics do not seem to be a major problem in current applied NLG systems (Mittal et al. 1998).

Background

acl_arc_intent_classification:train:1387

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: Other similar approaches include those of Cicekli and G¨uvenir (1996), McTait and Trujillo (1999), Carl (1999), and Brown (2000), inter alia. Watanabe (1993) combines lexical and dependency mappings to form his generalizations. Kaji, Kida, and Morimoto (1992) identify translationally equivalent phrasal segments and replace such equivalents with variables to generate a set of translation patterns. Citation Sentence: Other similar approaches include those of Cicekli and G ¨ uvenir ( 1996 ) , McTait and Trujillo ( 1999 ) , Carl ( 1999 ) , and Brown ( 2000 ) , inter alia . Context after the citation: In our system, in some cases the smallest chunk obtainable via the marker-based segmentation process may be something like (27): (27) <DET> the good man: le bon homme In such cases, if our system were confronted with a good man, it would not be able to translate such a phrase, assuming this to be missing from the marker lexicon. Accordingly, we convert examples such as (27) into their generalized equivalents, as in (28): (28) <DET> good man: bon homme That is, where Block (2000) substitutes variables for various words in his templates, we replace certain lexical items with their marker tag. Given that examples such as ’‘<DET> a : un” are likely to exist in the word-level lexicon, they may be inserted at the point indicated by the marker tag to form the correct translation un bon homme. We thus cluster on marker words to improve the coverage of our system (see Section 5 for results that show exactly how clustering on marker words helps); others (notably Brown [2000, 2003]) use clustering techniques to determine equivalence classes of individual words that can occur in the same context, and in so doing derive translation templates from individual translation examples.

Background

acl_arc_intent_classification:train:1388

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: 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. In this paper we focus on the Entity Detection and Recognition task (EDR) for Arabic as described in ACE 2004 framework (ACE, 2004). These tasks have applications in summarization, information retrieval (one can get all hits for Washington/person and not the ones for Washington/state or Washington/city), data mining, question answering, language understanding, etc. Citation Sentence: 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 . Context after the citation: 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. 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). An entity is the aggregate of all the mentions (of any level) which refer to one conceptual entity. For instance, in the sentence

Background

acl_arc_intent_classification:train:1389

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: Unlike our approach, those of Xia (1999) and Hockenmaier, Bierner, and Baldridge (2004) include a substantial initial correction and clean-up of the Penn-II trees. The algorithm handles “like” coordination and exploits the traces used in the treebank in order to interpret LDDs. Each node is subsequently assigned the relevant category based on its constituent type and surface configuration. Citation Sentence: Unlike our approach , those of Xia ( 1999 ) and Hockenmaier , Bierner , and Baldridge ( 2004 ) include a substantial initial correction and clean-up of the Penn-II trees . Context after the citation: Miyao, Ninomiya, and Tsujii (2004) and Nakanishi, Miyao, and Tsujii (2004) describe a methodology for acquiring an English HPSG from the Penn-II Treebank. Manually defined heuristics are used to automatically annotate each tree in the treebank with partially specified HPSG derivation trees: Head/argument/modifier distinctions are made for each node in the tree based on Magerman (1994) and Collins (1997); the whole tree is then converted to a binary tree; heuristics are applied to deal with phenomena such as LDDs and coordination and to correct some errors in the treebank, and finally an HPSG category is assigned to each node in the tree in accordance with its CFG category. In the next phase of the process (externalization), HPSG lexical entries are automatically extracted from the annotated trees through the application of “inverse schemata.”

CompareOrContrast

acl_arc_intent_classification:train:139

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 headwords, dictionary search through the pronunciation field is available; Carter (1987) has merged information from the pronunciation and hyphenation fields, creating an enhanced phonological representation which allows access to entries by broad phonetic class and syllable structure (Huttenlocher and Zue, 1983). From the master LDOCE file, we have computed alternative indexing information, which allows access into the dictionary via different routes. While no application currently makes use of this facility, the motivation for such an approach to dictionary access comes from envisaging a parser which will operate on the basis of the on-line LDOCE; and any serious parser must be able to recognise compounds before it segments its input into separate words. Citation Sentence: In addition to headwords , dictionary search through the pronunciation field is available ; Carter ( 1987 ) has merged information from the pronunciation and hyphenation fields , creating an enhanced phonological representation which allows access to entries by broad phonetic class and syllable structure ( Huttenlocher and Zue , 1983 ) . Context after the citation: In addition, a fully flexible access system allows the retrieval of dictionary entries on the basis of constraints specifying any combination of phonetic, lexical, syntactic, and semantic information (Boguraev et al., 1987). Independently, random selection of dictionary entries is also provided to allow the testing of software on an unbiased sample.

Background

acl_arc_intent_classification:train:1390

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 Hockenmaier, Bierner, and Baldridge (2002), Xia (1999), and Miyao, Ninomiya, and Tsujii (2004), we extract a reference lexicon from Sections 02–21 of the WSJ. This can be expressed as a measure of the coverage of the induced lexicon on new data. In addition to evaluating the quality of our extracted semantic forms, we also examined the rate at which they are induced. Citation Sentence: Following Hockenmaier , Bierner , and Baldridge ( 2002 ) , Xia ( 1999 ) , and Miyao , Ninomiya , and Tsujii ( 2004 ) , we extract a reference lexicon from Sections 02 -- 21 of the WSJ . Context after the citation: We then compare this to a test lexicon from Section 23. Table 27 shows the results of the evaluation of the coverage of an induced lexicon for verbs only. There is a corresponding semantic form in the reference lexicon for 89.89% of the verbs in Section 23. 10.11% of the entries in the test lexicon did not appear in the reference lexicon.

Uses

acl_arc_intent_classification:train:1391

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 only disambiguation metric that we used in our previous work (Marcu, 1997b) was the shape-based metric, according to which the &quot;best&quot; trees are those that are skewed to the right. The shape-based metric. In the light of the rhetorical-clustering-based metric, we consider that a discourse tree A is &quot;better&quot; than another discourse tree B if the sum of the rhetorical-clustering scores associated with the nodes of .4 is higher than the sum of the rhetorical-clustering scores associated with the nodes of B. Citation Sentence: The only disambiguation metric that we used in our previous work ( Marcu , 1997b ) was the shape-based metric , according to which the `` best '' trees are those that are skewed to the right . Context after the citation: The explanation for this metric is that text processing is, essentially, a left-to-right process. In many genres, people write texts so that the most important ideas go first, both at the paragraph and at the text levels) The more text writers add, the more they elaborate on the text that went before: as a consequence, incremental discourse building consists mostly of expansion of the right branches. According to the shape-based metric, we consider that a discourse tree A is &quot;better&quot; than another discourse tree B if A is more skewed to the right than B (see Marcu (1997c) for a mathematical formulation of the notion of skewedness).

Extends

acl_arc_intent_classification:train:1392

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: The need for information systems to support physicians at the point of care has been well studied (Covell et al., 1985; Gorman et al., 1994; Ely et al., 2005). For a variety of reasons, medicine is an interesting domain of research. This paper presents experiments with generative content models for analyzing the discourse structure of medical abstracts, which has been confirmed to follow the four-section pattern discussed above (Salanger-Meyer, 1990). Citation Sentence: The need for information systems to support physicians at the point of care has been well studied ( Covell et al. , 1985 ; Gorman et al. , 1994 ; Ely et al. , 2005 ) . Context after the citation: Retrieval techniques can have a large impact on how physicians access and leverage clinical evidence. Information that satisfies physicians’ needs can be found in the MEDLINE database maintained by the U.S. National Library of Medicine (NLM), which also serves as a readily available corpus of abstracts for our experiments. Furthermore, the availability of rich ontological resources, in the form of the Unified Medical Language System (UMLS) (Lindberg et al., 1993), and the availability of software that leverages this knowledge— MetaMap (Aronson, 2001) for concept identification and SemRep (Rindflesch and Fiszman, 2003) for relation extraction—provide a foundation for studying the role of semantics in various tasks.

Background

acl_arc_intent_classification:train:1393

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: Every arc always has a definite direction, i.e. arcs are arrows (Novak, 1998). Very briefly, concept maps show the relationships between concepts labelling both nodes and arcs. Concept mapping has been used at least in education for over thirty years, in particular at the Cornell University, where Piaget’s ideas gave the roots to the assimilation theory by David Ausubel. Citation Sentence: Every arc always has a definite direction , i.e. arcs are arrows ( Novak , 1998 ) . Context after the citation: In contrast, mind maps spread from a centre, with branches radiating out. Furthermore, mind maps, as thought and copyrighted by Tony Buzan, can label only nodes, not arcs. The resulting shape of mind maps is sometimes similar to neurons’ (Buzan, 2000). We have tested both concept and mind mapping software tools, available for free or in a trial period.

Background

acl_arc_intent_classification:train:1394

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 the first experiment, we use an induction algorithm (Hwa 2001a) based on the expectation-maximization (EM) principle that induces parsers for PLTIGs. 4.2.1 An Expectation-Maximization-Based Learner. This study is repeated for two different models, the PLTIG parser and Collins’s Model 2 parser. Citation Sentence: In the first experiment , we use an induction algorithm ( Hwa 2001a ) based on the expectation-maximization ( EM ) principle that induces parsers for PLTIGs . Context after the citation: The algorithm performs heuristic search through an iterative reestimation procedure to find local optima: sets of values for the grammar parameters that maximizes the grammar’s likelihood of generating the training data. In principle, the algorithm supports unsupervised learning; however, because the search space has too many local optima, the algorithm tends to converge on a model that is unsuitable for parsing. Here, we consider a partially supervised variant in which we assume that the learner is given the phrasal boundaries of the training sentences but not the label of the constituent units. For example, the sentence Several fund managers expect a rough market this morning before prices stabilize.

Uses

acl_arc_intent_classification:train:1395

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 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. The statistical significance test is performed by the re-sampling approach (Koehn, 2004). The translation quality is evaluated by case-insensitive BLEU-4 with the shortest length penalty. Citation Sentence: 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 . Context after the citation: The translation system used for testing the effectiveness of our U-trees is our in-house stringto-tree system (abbreviated as s2t). The system is implemented based on (Galley et al., 2006) and (Marcu et al. 2006). 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.

Uses

acl_arc_intent_classification:train:1396

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: 6 For Sent-Pred we also experimented with grammatical and sentence-based syntactic features, such as number of syntactic phrases, grammatical mood, and grammatical person (Marom and Zukerman 2006), but the simple binary bag-of-lemmas representation yielded similar results. During the We use a Support Vector Machine (SVM) with a Radial Basis Function kernel to predict SCs from users’ requests.7 A separate SVM is trained for each SC, with unigram and bigram lemmas in a request as input features, and a binary target feature specifying whether the SC contains a sentence from the response to this request. Citation Sentence: 6 For Sent-Pred we also experimented with grammatical and sentence-based syntactic features , such as number of syntactic phrases , grammatical mood , and grammatical person ( Marom and Zukerman 2006 ) , but the simple binary bag-of-lemmas representation yielded similar results . Context after the citation: 7 We employed the LIBSVM package (Chang and Lin 2001). 1. Calculate the scores of the sentences in the predicted SCs. 2.

Uses

acl_arc_intent_classification:train:1397

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: At present, the system uses a heuristic matching algorithm to classify relations into the appropriate category, though in the future we may consider a classifier similar to Nielsen et al. (2008). The diagnoser, based on Dzikovska et al. (2008b), outputs a diagnosis which consists of lists of correct, contradictory and non-mentioned objects and relations from the student’s answer. The knowledge base is used to check the factual correctness of the answers first, and then a diagnoser checks the explanation correctness. Citation Sentence: At present , the system uses a heuristic matching algorithm to classify relations into the appropriate category , though in the future we may consider a classifier similar to Nielsen et al. ( 2008 ) . Context after the citation:

FutureWork

acl_arc_intent_classification:train:1398

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: It is interesting to compare this analysis with that described in Dalrymple, Shieber, and Pereira (1991) and Pereira (1990, 1991). Citation Sentence: It is interesting to compare this analysis with that described in Dalrymple , Shieber , and Pereira ( 1991 ) and Pereira ( 1990 , 1991 ) . Context after the citation: Recall that in their treatment, quantified noun phrases are treated in two stages: firstly, what they call a &quot;free variable&quot; of type e is introduced in the NP position, with an associated &quot;quantifier assumption,&quot; which is added as a kind of premise. At a later stage the quantifier assumption is &quot;discharged,&quot; capturing all occurrences of the free variable. Thus their analysis of something like every manager disappeared would proceed as follows: every manager --= every(x,manager(x)) x disappeared disappear every manager disappeared every(x,manager(x)) disappear(x) discharge the assumption: every(x,manager(x),disappear(x)) In the final logical form, I am using an informal representation of their &quot;pair&quot; notation for generalized quantifiers, which uses the same variable in both the restriction and the body, unlike the one we have been using. If we make a comparison between the way phenomena such as antecedent contained deletion are treated in our two frameworks, we can see that we also need such a notational change.

CompareOrContrast

acl_arc_intent_classification:train:1399

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 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). MT system combination (Matusov, Ueffing, and Ney 2006). 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 Citation Sentence: 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 ) . Context after the citation: 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. Many researchers use the GIZA++ software package (Och and Ney 2003) as a black box, selecting IBM Model 4 as a compromise between alignment quality and efficiency. All of the models are asymmetric (switching target and source languages produces drastically different results) and the simpler models (IBM Models 1, 2, and HMM) do not enforce bijectivity (the majority of words translating as a single word).

Motivation

acl_arc_intent_classification:train:14

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: 2The algorithm was implemented by the the authors, following the description in Hepple (2000). t, and C[s] = T [s]g the samples on which the rule applies and changes the classification from correct to incorrect; similarly, bad(r) = jB(r)j. Given a newly learned rule b that is to be applied to S, the goal is to identify the rules r for which at least one of the sets G (r) , B (r) is modified by the application of rule b. Obviously, if both sets are not modified when applying rule b, then the value of the objective function for rule r remains unchanged. • B (r) = fs 2 Sjp,(s) = true and C[s] =6 Citation Sentence: 2The algorithm was implemented by the the authors , following the description in Hepple ( 2000 ) . Context after the citation: The presentation is complicated by the fact that, in many NLP tasks, the samples are not independent. For instance, in POS tagging, a sample is dependent on the classification of the preceding and succeeding 2 samples (this assumes that there exists a natural ordering of the samples in S). Let V (s) denote the &quot;vicinity&quot; of a sample the set of samples on whose classification the sample s might depend on (for consistency, s 2 V (s)); if samples are independent, then V (s) = fsg.

Uses

acl_arc_intent_classification:train:140

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: The importance of including nonheadwords has become uncontroversial (e.g. Collins 1999; Charniak 2000; Goodman 1998). While the models of Collins (1996) and Eisner (1996) restricted the fragments to the locality of head-words, later models showed the importance of including context from higher nodes in the tree (Charniak 1997; Johnson 1998a). However, during the last few years we can observe a shift towards using more and larger corpus fragments with fewer restrictions. Citation Sentence: The importance of including nonheadwords has become uncontroversial ( e.g. Collins 1999 ; Charniak 2000 ; Goodman 1998 ) . Context after the citation: And Collins (2000) argues for &quot;keeping track of counts of arbitrary fragments within parse trees&quot;, which has indeed been carried out in Collins and Duffy (2002) who use exactly the same set of (all) tree fragments as proposed in Bod (1992). Thus the major innovations of DOP are: 1. the use of corpus fragments rather than grammar rules,

Background

acl_arc_intent_classification:train:1400

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: Some methods of semantic relation analysis rely on predefined templates filled with information from processed texts (Baker et al., 1998). Citation Sentence: Some methods of semantic relation analysis rely on predefined templates filled with information from processed texts ( Baker et al. , 1998 ) . Context after the citation: In other methods, lexical resources are specifically tailored to meet the requirements of the domain (Rosario and Hearst, 2001) or the system (Gomez, 1998). Such systems extract information from some types of syntactic units (clauses in (Fillmore and Atkins, 1998; Gildea and Jurafsky, 2002; Hull and Gomez, 1996); noun phrases in (Hull and Gomez, 1996; Rosario et al., 2002)). Lists of semantic relations are designed to capture salient domain information. In the Rapid Knowledge Formation Project (RKF) a support system was developed for domain experts.

Background

acl_arc_intent_classification:train:1401

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 example used to illustrate the power of ATNs (Woods 1986), &quot;John was believed to have been shot,&quot; also parses correctly, because the [object] node following the verb &quot;believed&quot; acts as both an absorber and a (re)generator. The phrase &quot;which hospital&quot; is finally absorbed by the preposition's object. Meanwhile, the [participial-phrase] passes along the original FLOATOBJECT (&quot;which hospital&quot;) to its right sibling, the adverbial prepositional phrase, &quot;to [object].&quot; Citation Sentence: The example used to illustrate the power of ATNs ( Woods 1986 ) , `` John was believed to have been shot , '' also parses correctly , because the [ object ] node following the verb `` believed '' acts as both an absorber and a ( re ) generator . Context after the citation: Cases of crossed traces are automatically blocked because the second CURRENT-FOCUS gets moved into the FLOAT-OBJECT position at the time of the second activator, overriding the preexisting FLOAT-OBJECT set up by the earlier activator. The wrong FLOAT-OBJECT is available at the position of the first trace, and the parse dies: *(Which books)i did you ask John (where)1 Bill bought (t,) (tj)? Example of a parse tree illustrating a gap. The CURRENT-FOCUS slot is not restricted to nodes that represent nouns.

CompareOrContrast

acl_arc_intent_classification:train:1402

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: Problems such as these have motivated research on more abstract, dependencybased parser evaluation (e.g., Lin 1995; Carroll, Briscoe, and Sanfilippo 1998; Carroll et al. 2002; Clark and Hockenmaier 2002; King et al. 2003; Preiss 2003; Kaplan et al. 2004; Miyao and Tsujii 2004). Because a tree-based gold standard for parser evaluation must adopt a particular style of linguistic analysis (reflected in the geometry and nomenclature of the nodes in the trees), evaluation of statistical parsers and grammars that are derived from particular treebank resources (as well as hand-crafted grammars/parsers) can suffer unduly if the gold standard deviates systematically from the (possibly) equally valid style of linguistic analysis provided by the parser. 3. Citation Sentence: Problems such as these have motivated research on more abstract , dependencybased parser evaluation ( e.g. , Lin 1995 ; Carroll , Briscoe , and Sanfilippo 1998 ; Carroll et al. 2002 ; Clark and Hockenmaier 2002 ; King et al. 2003 ; Preiss 2003 ; Kaplan et al. 2004 ; Miyao and Tsujii 2004 ) . Context after the citation: Dependency-based linguistic representations are approximations of abstract predicate-argument-adjunct (or more basic head-dependent) structures, providing a more normalized representation abstracting away from the particulars of surface realization or CFG-tree representation, which enables meaningful cross-parser evaluation. A related contrast holds between shallow and deep grammars and parsers.1 In addition to defining a language (as a set of strings), deep grammars relate strings to information/meaning, often in the form of predicate–argument structure, dependency relations,2 or logical forms. By contrast, a shallow grammar simply defines a language and may associate syntactic (e.g., CFG tree) representations with strings.

Motivation

acl_arc_intent_classification:train:1403

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: LiLFeS is one of the fastest inference engines for processing feature structure logic, and efficient HPSG parsers have already been built on this system (Nishida et al., 1999; Torisawa et al., 2000). The RenTAL system is implemented in LiLFeS (Makino et al., 1998)2. Citation Sentence: LiLFeS is one of the fastest inference engines for processing feature structure logic , and efficient HPSG parsers have already been built on this system ( Nishida et al. , 1999 ; Torisawa et al. , 2000 ) . Context after the citation: We applied our system to the XTAG English grammar (The XTAG Research Group, 2001)3, which is a large-scale FB-LTAG grammar for English. 2The RenTAL system is available at: http://www-tsujii.is.s.u-tokyo.ac.jp/rental/ 3We used the grammar attached to the latest distribution of an LTAG parser which we used for the parsing experiment. The parser is available at: ftp://ftp.cis.upenn.edu/pub/xtag/lem/lem-0.13.0.i686.tgz The XTAG English grammar consists of 1,1944 elementary tree templates and around 45,000 lexical items5.

Background

acl_arc_intent_classification:train:1404

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: As in (Lee et al., 2003), we used unsupervised training data which is automatically segmented to discover previously unseen stems. We seeked to exploit this ability to generalize to improve 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. Citation Sentence: As in ( Lee et al. , 2003 ) , we used unsupervised training data which is automatically segmented to discover previously unseen stems . Context after the citation: In our case, the character n-gram model is used to segment a portion of the Arabic Gigaword corpus. From this, we create a vocabulary of stems and affixes by requiring that tokens appear more than twice in the supervised training data or more than ten times in the unsupervised, segmented corpus. The resulting vocabulary, predominately of word stems, is 53K words, or about six times the vocabulary observed in the supervised training data. This represents about only 18% of the total number of unique tokens observed in the aggregate training data.

Uses

acl_arc_intent_classification:train:1405

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: To address this limitation, our previous work (Zhang and Chai, 2009) has initiated an investigation on the problem of conversation entailment. (2) B is eighty-three. Hypothesis: (1) B’s mother is eighty-three. Citation Sentence: To address this limitation , our previous work ( Zhang and Chai , 2009 ) has initiated an investigation on the problem of conversation entailment . Context after the citation: The problem was formulated as follows: given a conversation discourse D and a hypothesis H concerning its participant, the goal was to identify whether D entails H. For instance, as in Example 1, the first hypothesis can be entailed from the conversation segment while the second hypothesis cannot. While our previous work has provided some interesting preliminary observations, it mostly focused on data collection and initial experiments and analysis using a small set of development data. It is not clear whether the previous results are generally applicable, how different components in the entailment framework interact with each other, and how different representations may influence the entailment outcome.

Extends

acl_arc_intent_classification:train:1406

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 brief version of this work, with some additional material, first appeared as (Eisner, 2001a). It is common to define further useful operations (as macros), which modify existing relations not by editing their source code but simply by operating on them “from outside.” Its unusual flexibility for the practiced programmer stems from the many operations under which rational relations are closed. Citation Sentence: ∗ A brief version of this work , with some additional material , first appeared as ( Eisner , 2001a ) . Context after the citation: A leisurely journal-length version with more details has been prepared and is available. The entire paradigm has been generalized to weighted relations, which assign a weight to each (input, output) pair rather than simply including or excluding it. If these weights represent probabilities P(input, output) or P(output |input), the weighted relation is called a joint or conditional (probabilistic) relation and constitutes a statistical model. Such models can be efficiently restricted, manipulated or combined using rational operations as before.

Extends

acl_arc_intent_classification:train:1407

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: They also proposed two major categories of meta-learning approaches for recommender systems, merging and ensemble, each subdivided into the more specific subclasses suggested by Burke (2002) as follows. Lekakos and Giaglis (2007) implemented a supervised version of this approach for a recommender system, as opposed to our unsupervised version. This kind of meta-learning is referred to as stacking by the Data Mining community (Witten and Frank 2000). Citation Sentence: They also proposed two major categories of meta-learning approaches for recommender systems , merging and ensemble , each subdivided into the more specific subclasses suggested by Burke ( 2002 ) as follows . Context after the citation: The merging category corresponds to techniques where the individual methods affect each other in different ways (this category encompasses Burke’s feature combination, cascade, feature augmentation, and meta-level sub-categories). The ensemble category corresponds to techniques where the predictions of the individual methods are combined to produce a final prediction (this category encompasses Burke’s weighted, switching, and mixed sub-categories). Our system falls into the ensemble category, because it combines the results of the various methods into a single outcome. More specifically, it belongs to Burke’s switching sub-category, where a single method is selected on a case-by-case basis.

Background

acl_arc_intent_classification:train:1408

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: Here, I adopt the model proposed by Marantz (1997) and decompose lexical verbs into verbalizing heads and verbal roots. Following the non-lexicalist tradition, these primitives are argued to occupy functional projections in the syntactic structure, as so-called light verbs. They correspond to the primitives BE, BECOME, and DO proposed by a variety of linguists; let us adopt these conceptual primitives as the basic vocabulary of our lexical semantic representation. Citation Sentence: Here , I adopt the model proposed by Marantz ( 1997 ) and decompose lexical verbs into verbalizing heads and verbal roots . Context after the citation: Verbalizing heads introduce relevant eventive interpretations in the syntax, and correspond to (assumed) universal primitives of the human cognitive system. On the other hand, verbal roots represent abstract (categoryless) concepts and basically correspond to open-class items drawn from encyclopedic knowledge. I assume an inventory of three verbalizing heads, each corresponding to an aforementioned primitive: (9) vDO [+dynamic, −inchoative] = DO vb [+dynamic, +inchoative] = BECOME vBE [−dynamic] = BE

Uses

acl_arc_intent_classification:train:1409

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: Our motivation for generation of material for language education exists in work such as Sumita et al. (2005) and Mostow and Jang (2012), which deal with automatic generation of classic fill in the blank questions. Examples such as ¨Ozbal et al. (2013) and Valitutti et al. (2013) use template filling techniques guided by quantified notions of humor or how catchy a phrase is. Computational creativity is another subfield of NLG that often does not fix an a priori meaning in its output. Citation Sentence: Our motivation for generation of material for language education exists in work such as Sumita et al. ( 2005 ) and Mostow and Jang ( 2012 ) , which deal with automatic generation of classic fill in the blank questions . Context after the citation: Our work is naturally complementary to these efforts, as their methods require a corpus of in-vocab text to serve as seed sentences.

Motivation

acl_arc_intent_classification:train:141

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 heuristic is called soft union (DeNero and Klein 2007). We include a point in the final alignment if the average of the posteriors under the two models for that point is above a threshold. the knowledge about the posterior distributions of each directional model. Citation Sentence: This heuristic is called soft union ( DeNero and Klein 2007 ) . Context after the citation: Figure 8 shows the Precision/Recall curves after symmetrization for the En-Fr corpus. The posterior regularization–trained models still performed better, but the differences get smaller after doing the symmetrization. This should not be very surprising, because the soft union symmetrization can be viewed as an approximation of our symmetry constraint applied only at decode time. Applying the symmetrization to the model with symmetry constraints does not affect performance.

Uses

acl_arc_intent_classification:train:1410

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 approach has its roots in Fillmore’s Case Grammar (1968), and serves as the foundation for two current large-scale semantic annotation projects: FrameNet (Baker et al., 1998) and PropBank (Kingsbury et al., 2002). Verbs are viewed as simple predicates over their arguments. A common lexical semantic representation in the computational linguistics literature is a frame-based model where syntactic arguments are associated with various semantic roles (essentially frame slots). Citation Sentence: This approach has its roots in Fillmore 's Case Grammar ( 1968 ) , and serves as the foundation for two current large-scale semantic annotation projects : FrameNet ( Baker et al. , 1998 ) and PropBank ( Kingsbury et al. , 2002 ) . Context after the citation: Underlying the semantic roles approach is a lexicalist assumption, that is, each verb’s lexical entry completely encodes (more formally, projects) its syntactic and semantic structures. Alternations in argument structure are usually attributed to multiple lexical entries (i.e., verb senses). Under the lexicalist approach, the semantics of the verb break might look something like this: (1) break(agent, theme) agent: subject theme: object break(agent, theme, instrument) agent: subject theme: object instrument: oblique(with) break(theme) theme: subject . . .

Background

acl_arc_intent_classification:train:1411

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:1412

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: The ability to explicitly identify these sections in unstructured text could play an important role in applications such as document summarization (Teufel and Moens, 2000), information retrieval (Tbahriti et al., 2005), information extraction (Mizuta et al., 2005), and question answering. fields generally follow the pattern of “introduction”, “methods”, “results”, and “conclusions” (SalangerMeyer, 1990; Swales, 1990; Or˘asan, 2001). As an example, scientific abstracts across many different Citation Sentence: The ability to explicitly identify these sections in unstructured text could play an important role in applications such as document summarization ( Teufel and Moens , 2000 ) , information retrieval ( Tbahriti et al. , 2005 ) , information extraction ( Mizuta et al. , 2005 ) , and question answering . Context after the citation: Although there is a trend towards analysis of full article texts, we believe that abstracts still provide a tremendous amount of information, and much value can still be extracted from them. For example, Gay et al. (2005) experimented with abstracts and full article texts in the task of automatically generating index term recommendations and discovered that using full article texts yields at most a 7.4% improvement in F-score. Demner-Fushman et al. (2005) found a correlation between the quality and strength of clinical conclusions in the full article texts and abstracts. This paper presents experiments with generative content models for analyzing the discourse structure of medical abstracts, which has been confirmed to follow the four-section pattern discussed above (Salanger-Meyer, 1990).

Background

acl_arc_intent_classification:train:1413

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 semantics was constructed (Zadrozny 1987a, 1987b) as a formal framework for default and commonsense reasoning. We adopt the three-level semantics as a formal tool for the analysis of paragraphs. where s, m, d, are constants. Citation Sentence: This semantics was constructed ( Zadrozny 1987a , 1987b ) as a formal framework for default and commonsense reasoning . Context after the citation: It should not come as a surprise that we can now use this apparatus for text/discourse analysis; after all, many natural language inferences are based on defaults, and quite often they can be reduced to choosing most plausible interpretations of predicates. For instance, relating &quot;they&quot; to &quot;apples&quot; in the sentence (cfXXX Haugeland 1985 p. 195; Zadrozny 1987a): We bought the boys apples because they were so cheap can be an example of such a most plausible choice. The main ideas of the three-level semantics can be stated as follows:

Uses

acl_arc_intent_classification:train:1414

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: There is a general consensus among theoretical linguists that the proper representation of verbal argument structure is event structure—representations grounded in a theory of events that decompose semantic roles in terms of primitive predicates representing concepts such as causality and inchoativity (Dowty, 1979; Jackendoff, 1983; Pustejovsky, 1991b; Rappaport Hovav and Levin, 1998). Fixed roles are too coarsegrained to account for certain semantic distinctions—the only recourse, to expand the inventory of roles, comes with the price of increased complexity, e.g., in the syntaxto-semantics mapping. The actual inventory of semantic roles, along with precise definitions and diagnostics, remains an unsolved problem; see (Levin and Rappaport Hovav, 1996). Citation Sentence: There is a general consensus among theoretical linguists that the proper representation of verbal argument structure is event structure -- representations grounded in a theory of events that decompose semantic roles in terms of primitive predicates representing concepts such as causality and inchoativity ( Dowty , 1979 ; Jackendoff , 1983 ; Pustejovsky , 1991b ; Rappaport Hovav and Levin , 1998 ) . Context after the citation: Consider the following example: (2) He sweeps the floor clean. [ [ DO(he, sweeps(the floor)) ] CAUSE [ BECOME [ clean(the floor) ] ] ] Dowty breaks the event described by (2) into two subevents, the activity of sweeping the floor and its result, the state of the floor being clean. A more recent approach, advocated by Rappaport Hovav and Levin (1998), describes a basic set of event templates corresponding to Vendler’s event classes (Vendler, 1957): (3) a. [ x ACT<MANNER> ] (activity) b. [ x <STATE> ] (state) c. [ BECOME [ x <STATE> ] ] (achievement) d. [ x CAUSE [ BECOME [ x <STATE> ] ] ] (accomplishment)

Background

acl_arc_intent_classification:train:1415

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: Similarly, the notion of R + M-abduction is spiritually related to the &quot;abductive inference&quot; of Reggia (1985), the &quot;diagnosis from first principles&quot; of Reiter (1987), &quot;explainability&quot; of Poole (1988), and the subset principle of Berwick (1986). , &quot;domain circumscription&quot; (cfXXX Etherington and Mercer 1987), and their kin. Since techniques developed elsewhere may prove useful, at least for comparison, it is worth mentioning at this point that the proposed metarules are distant cousins of &quot;unique-name assumption&quot; (Genesereth and Nilsson 1987), &quot;domain closure assumption&quot; (ibid.) Citation Sentence: Similarly , the notion of R + M-abduction is spiritually related to the `` abductive inference '' of Reggia ( 1985 ) , the `` diagnosis from first principles '' of Reiter ( 1987 ) , `` explainability '' of Poole ( 1988 ) , and the subset principle of Berwick ( 1986 ) . Context after the citation: But, obviously, trying to establish precise connections for the metarules or the provability and the R + M-abduction would go much beyond the scope of an argument for the correspondence of paragraphs and models. These connections are being examined elsewhere (Zadrozny forthcoming).

CompareOrContrast

acl_arc_intent_classification:train:1416

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: 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. In this paper we focus on the Entity Detection and Recognition task (EDR) for Arabic as described in ACE 2004 framework (ACE, 2004). These tasks have applications in summarization, information retrieval (one can get all hits for Washington/person and not the ones for Washington/state or Washington/city), data mining, question answering, language understanding, etc. Citation Sentence: 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 . Context after the citation: 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. 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). An entity is the aggregate of all the mentions (of any level) which refer to one conceptual entity. For instance, in the sentence

Background

acl_arc_intent_classification:train:1417

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: Representative systems are described in Boisen et al. (1989), De Mattia and Giachin (1989), Niedermair (1989), Niemann (1990), and Young (1989). Current advances in research and development of spoken language systems2 can be found, for example, in the proceedings of the DARPA speech and natural language workshops, as well as in publications from participants of the ESPRIT SUNDIAL project. In fact, to be truly effective, many potential applications demand that the system carry on a dialog with the user, using its knowledge base and information gleaned from previous sentences to achieve proper response generation. Citation Sentence: Representative systems are described in Boisen et al. ( 1989 ) , De Mattia and Giachin ( 1989 ) , Niedermair ( 1989 ) , Niemann ( 1990 ) , and Young ( 1989 ) . Context after the citation: * Spoken Language Systems Group, Laboratoiy for Computer Science, MIT, Cambridge MA 02139 f This research was supported by DARPA under Contract N00014-89+1332, monitored through the Office of Naval Research. 1 Speech understanding research flourished in the U.S. in the 1970s under DARPA sponsorship. While &quot;understanding&quot; was one of the original goals, none of the systems really placed any emphasis on this aspect of the problem. 2 We will use the term &quot;speech understanding systems&quot; and &quot;spoken language systems&quot; interchangeably.

Background

acl_arc_intent_classification:train:1418

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: R98(,,,,„) uses a variant of Kozima's semantic similarity measure (Kozima, 1993) to compute block similarity. R98(,„,d0t) is the modularised version of R98 for experimenting with different similarity measures. in section 3.4. Citation Sentence: R98 ( , , , , „ ) uses a variant of Kozima 's semantic similarity measure ( Kozima , 1993 ) to compute block similarity . Context after the citation: Word similarity is a function of word cooccurrence statistics in the given document. Words that belong to the same sentence are considered to be related. Given the co-occurrence frequencies f (wi, wi), the transition probability matrix t is computed by equation 10. Equation 11 defines our spread activation scheme.

Extends

acl_arc_intent_classification:train:1419

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 most recent research, NEs (person, location and organisations) are extracted from the text and used as a source of evidence to calculate the similarity between documents -see for instance (Blume, 2005; Chen and Martin, 2007; Popescu and Magnini, 2007; Kalashnikov et al., 2007). Ravin (1999) introduced a rule-based approach that tackles both variation and ambiguity analysing the structure of names. The most used feature for the Web People Search task, however, are NEs. Citation Sentence: In most recent research , NEs ( person , location and organisations ) are extracted from the text and used as a source of evidence to calculate the similarity between documents - see for instance ( Blume , 2005 ; Chen and Martin , 2007 ; Popescu and Magnini , 2007 ; Kalashnikov et al. , 2007 ) . Context after the citation: For instance, Blume (2005) uses NEs coocurring with the ambiguous mentions of a name as a key feature for the disambiguation process. Saggion (2008) compared the performace of NEs versus BoW features. In his experiments a only a representation based on Organisation NEs outperformed the word based approach. Furthermore, this result is highly dependent on the choice of metric weighting (NEs achieve high precision at the cost of a low recall and viceversa for BoW).

Background

acl_arc_intent_classification:train:142

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: Liu et al. (2005), Meral et al. (2007), Murphy (2001), Murphy and Vogel (2007) and Topkara et al. (2006a) all belong to the syntactic transformation category. In other words, instead of performing lexical substitution directly to the text, the secret message is embedded into syntactic parse trees of the sentences. Later, Atallah et al. (2001b) embedded information in the tree structure of the text by adjusting the structural properties of intermediate representations of sentences. Citation Sentence: Liu et al. ( 2005 ) , Meral et al. ( 2007 ) , Murphy ( 2001 ) , Murphy and Vogel ( 2007 ) and Topkara et al. ( 2006a ) all belong to the syntactic transformation category . Context after the citation: After embedding the secret message, modified deep structure forms are converted into the surface structure format via language generation tools. Atallah et al. (2001b) and Topkara et al. (2006a) attained the embedding capacity of 0.5 bits per sentence with the syntactic transformation method.

Background

acl_arc_intent_classification:train:1420

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 used a publicly available tagger (Ratnaparkhi, 1996) to tag the words and then used these in the input to the system. 5In these experiments the tags are included in the input to the system, but, for compatibility with other parsers, we did not use the hand-corrected tags which come with the corpus. We then tested the best non-lexicalized and the best lexicalized models on the testing set.6 Standard measures of performance are shown in table 1.7 The top panel of table 1 lists the results for the non-lexicalized model (SSN-Tags) and the available results for three other models which only use part-of-speech tags as inputs, another neural network parser (Costa et al., 2001), an earlier statis- Citation Sentence: We used a publicly available tagger ( Ratnaparkhi , 1996 ) to tag the words and then used these in the input to the system . Context after the citation: 6We found that 80 hidden units produced better performance than 60 or 100. Momentum was applied throughout training. Weight decay regularization was applied at the beginning of training but reduced to zero by the end of training. 7A11 our results are computed with the evalb program following the now-standard criteria in (Collins, 1999).

Uses

acl_arc_intent_classification:train:1421

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: Some works abstract perception via the usage of symbolic logic representations (Chen et al., 2010; Chen and Mooney, 2011; Matuszek et al., 2012; Artzi and Zettlemoyer, 2013), while others choose to employ concepts elicited from psycholinguistic and cognition studies. Many approaches to multimodal research have succeeded by abstracting away raw perceptual information and using high-level representations instead. The language grounding problem has received significant attention in recent years, owed in part to the wide availability of data sets (e.g. Flickr, Von Ahn (2006)), computing power, improved computer vision models (Oliva and Torralba, 2001; Lowe, 2004; Farhadi et al., 2009; Parikh and Grauman, 2011) and neurological evidence of ties between the language, perceptual and motor systems in the brain (Pulverm¨uller et al., 2005; Tettamanti et al., 2005; Aziz-Zadeh et al., 2006). Citation Sentence: Some works abstract perception via the usage of symbolic logic representations ( Chen et al. , 2010 ; Chen and Mooney , 2011 ; Matuszek et al. , 2012 ; Artzi and Zettlemoyer , 2013 ) , while others choose to employ concepts elicited from psycholinguistic and cognition studies . Context after the citation: Within the latter category, the two most common representations have been association norms, where subjects are given a 1http://stephenroller.com/research/ emnlp13 cue word and name the first (or several) associated words that come to mind (e.g., Nelson et al. (2004)), and feature norms, where subjects are given a cue word and asked to describe typical properties of the cue concept (e.g., McRae et al. (2005)). Griffiths et al. (2007) helped pave the path for cognitive-linguistic multimodal research, showing that Latent Dirichlet Allocation outperformed Latent Semantic Analysis (Deerwester et al., 1990) in the prediction of association norms.

Background

acl_arc_intent_classification:train:1422

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: Multilingual lexical databases aligned with the English WordNet (e.g. MultiWordNet (Pianta et al., 2002)) have been created for several languages, with different degrees of coverage. However, with the only exceptions represented by WordNet and Wikipedia, most of the aforementioned resources are available only for English. This situation makes the availability of multilingual lexical knowledge a necessary condition to bridge the language gap. Citation Sentence: Multilingual lexical databases aligned with the English WordNet ( e.g. MultiWordNet ( Pianta et al. , 2002 ) ) have been created for several languages , with different degrees of coverage . Context after the citation: As an example, the 57,424 synsets of the Spanish section of MultiWordNet aligned to English cover just around 50% of the WordNet’s synsets, thus making the coverage issue even more problematic than for TE. As regards Wikipedia, the crosslingual links between pages in different languages offer a possibility to extract lexical knowledge useful for CLTE. However, due to their relatively small number (especially for some languages), bilingual lexicons extracted from Wikipedia are still inadequate to provide acceptable coverage. In addition, featuring a bias towards named entities, the information acquired through cross-lingual links can at most complement the lexical knowledge extracted from more generic multilingual resources (e.g bilingual dictionaries).

Background

acl_arc_intent_classification:train:1423

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:1424

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 future work, we might investigate how machine learning algorithms, which are specifically designed for the problem of domain adaptation (Blitzer et al., 2007; Jiang and Zhai, 2007), perform in comparison to our approach. The features we employ scale well across domains, given that the opinion target vocabularies are substantially different. Our CRF-based approach also yields promising results in the crossdomain setting. Citation Sentence: For future work , we might investigate how machine learning algorithms , which are specifically designed for the problem of domain adaptation ( Blitzer et al. , 2007 ; Jiang and Zhai , 2007 ) , perform in comparison to our approach . Context after the citation: Since three of the features we employed in our CRF-based approach are based on the respective opinion expressions, it is to investigate how to mitigate the possible negative effects introduced by errors in the opinion expression identification if they are not annotated in the gold standard. We observe similar challenges as Choi et al. (2005) regarding the analysis of complex sentences. Although our data is user-generated from Web 2.0 communities, a manual inspection has shown that the documents were of relatively high textual quality.

FutureWork

acl_arc_intent_classification:train:1425

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 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). MT system combination (Matusov, Ueffing, and Ney 2006). 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 Citation Sentence: 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 ) . Context after the citation: 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. Many researchers use the GIZA++ software package (Och and Ney 2003) as a black box, selecting IBM Model 4 as a compromise between alignment quality and efficiency. All of the models are asymmetric (switching target and source languages produces drastically different results) and the simpler models (IBM Models 1, 2, and HMM) do not enforce bijectivity (the majority of words translating as a single word).

Motivation

acl_arc_intent_classification:train:1426

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: That is, if the current hypothesis is unable to label a candidate or is uncertain about it, then the candidate might be a good training example (Lewis and Catlett 1994). Performance of the hypothesis: Testing the candidates on the current working hypothesis shows the type of input data on which the hypothesis may perform weakly. 2. Citation Sentence: That is , if the current hypothesis is unable to label a candidate or is uncertain about it , then the candidate might be a good training example ( Lewis and Catlett 1994 ) . Context after the citation: The underlying assumption is that an uncertain output is likely to be wrong. 3. Parameters of the hypothesis: Estimating the potential impact that the candidates will have on the parameters of the current working hypothesis locates those examples that will change the current hypothesis the most. Pseudo code for the sample selection learning algorithm.

Background

acl_arc_intent_classification:train:1427

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 milestones of recent research include the deployment of probabilistic and machine learning techniques (Aone and Bennett 1995; Kehler 1997; Ge, Hale, and Charniak 1998; Cardie and Wagstaff 1999; the continuing interest in centering, used either in original or in revised form (Abracos and Lopes 1994; Strube and Hahn 1996; Hahn and Strube 1997; Tetreault 1999); and proposals related to the evaluation methodology in anaphora resolution (Mitkov 1998a, 2001b). Against the background of a growing interest in multilingual NLP, multilingual anaphora /coreference resolution has gained considerable momentum in recent years (Aone and McKee 1993; Azzam, Humphreys, and Gaizauskas 1998; Harabagiu and Maiorano 2000; Mitkov and Barbu 2000; Mitkov 1999; Mitkov and Stys 1997; Mitkov, Belguith, and Stys 1998). The last decade of the 20th century saw a number of anaphora resolution projects for languages other than English such as French, German, Japanese, Spanish, Portuguese, and Turkish. Citation Sentence: Other milestones of recent research include the deployment of probabilistic and machine learning techniques ( Aone and Bennett 1995 ; Kehler 1997 ; Ge , Hale , and Charniak 1998 ; Cardie and Wagstaff 1999 ; the continuing interest in centering , used either in original or in revised form ( Abracos and Lopes 1994 ; Strube and Hahn 1996 ; Hahn and Strube 1997 ; Tetreault 1999 ) ; and proposals related to the evaluation methodology in anaphora resolution ( Mitkov 1998a , 2001b ) . Context after the citation: For a more detailed survey of the state of the art in anaphora resolution, see Mitkov (forthcoming). The papers published in this issue reflect the major trends in anaphora resolution in recent years. Some of them describe approaches that do not exploit full syntactic knowledge (as in the case of Palomar et al.'s and Stuckardt's work) or that employ machine learning techniques (Soon, Ng, and Lim); others present centering-based pronoun resolution (Tetreault) or discuss theoretical centering issues (Kibble). Almost all of the papers feature extensive evaluation (including comparative evaluation as in the case of Tetreault's and Palomar et al.'s work) or discuss general evaluation issues (Byron as well as Stuckardt).

Background

acl_arc_intent_classification:train:1428

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 instance, the derived morphological forms are believed to be represented as a whole, whereas the representation of the inflected forms follows the morphemic model (Caramazza et al., 1988). Intermediate to these two paradigms is the partial decomposition model that argues that different types of morphological forms are processed separately. The affixes are stripped away from the root form, which in turn are used to access the mental lexicon (Taft and Forster, 1975; Taft, 1981; MacKay, 1978). Citation Sentence: For instance , the derived morphological forms are believed to be represented as a whole , whereas the representation of the inflected forms follows the morphemic model ( Caramazza et al. , 1988 ) . Context after the citation: Traditionally, priming experiments have been used to study the effects of morphology in language processing. Priming is a process that results in increase in speed or accuracy of response to a stimulus, called the target, based on the occurrence of a prior exposure of another stimulus, called the prime (Tulving et al., 1982). Here, subjects are exposed to a prime word for a short duration, and are subsequently shown a target word. The prime and target words may be morphologically, phonologically or semantically re-

Background

acl_arc_intent_classification:train:1429

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: Many researchers use the GIZA++ software package (Och and Ney 2003) as a black box, selecting IBM Model 4 as a compromise between alignment quality and efficiency. 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. 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. Citation Sentence: Many researchers use the GIZA + + software package ( Och and Ney 2003 ) as a black box , selecting IBM Model 4 as a compromise between alignment quality and efficiency . Context after the citation: All of the models are asymmetric (switching target and source languages produces drastically different results) and the simpler models (IBM Models 1, 2, and HMM) do not enforce bijectivity (the majority of words translating as a single word). Although there are systematic translation phenomena where one cannot hope to obtain 1-to-1 alignments, we observe that in over 6 different European language pairs the majority of alignments are in fact 1-to-1 (86–98%). This leads to the common practice of post-processing heuristics for intersecting directional alignments to produce nearly bijective and symmetric results (Koehn, Och, and Marcu 2003). In this article we focus on the HMM word alignment model (Vogel, Ney, and Tillmann 1996), using a novel unsupervised learning framework that significantly boosts its performance.

Background

acl_arc_intent_classification:train:143

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: We perceive that these results can be extended to other language models that properly embed bilexical context-free grammars, as for instance the more general history-based models used in (Ratnaparkhi, 1997) and (Chelba and Jelinek, 1998). Our results hold for bilexical context-free grammars and directly transfer to several language models that can be seen as stochastic versions of this formalism (see Section 1). In this paper we have provided an original mathematical argument in favour of this thesis. Citation Sentence: We perceive that these results can be extended to other language models that properly embed bilexical context-free grammars , as for instance the more general history-based models used in ( Ratnaparkhi , 1997 ) and ( Chelba and Jelinek , 1998 ) . Context after the citation: We leave this for future work.

FutureWork

acl_arc_intent_classification:train:1430

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: Another paper (Yoshinaga et al., 2001) describes the detailed analysis on the factor of the difference of parsing performance. We can say that the grammar conversion enables us to share HPSG parsing techniques in LTAG parsing. parsing. Citation Sentence: Another paper ( Yoshinaga et al. , 2001 ) describes the detailed analysis on the factor of the difference of parsing performance . Context after the citation:

Background

acl_arc_intent_classification:train:1431

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 original and the obtained grammar generated exactly the same number of derivation trees in the parsing experiment with 457 sentences from the ATIS corpus (Marcus et al., 1994)6 (the average length is 6.32 words). Conversion took about 25 minutes CPU time on a 700 Mhz Pentium III Xeon with four gigabytes main memory. Table 1 shows the classifications of elementary tree templates of the XTAG English grammar, according to the conditions we introduced in Section 3, and also shows the number of corresponding HPSG lexical entry templates. Citation Sentence: The original and the obtained grammar generated exactly the same number of derivation trees in the parsing experiment with 457 sentences from the ATIS corpus ( Marcus et al. , 1994 ) 6 ( the average length is 6.32 words ) . Context after the citation: This result empirically attested the strong equivalence of our algorithm. Table 2 shows the average parsing time with the LTAG and HPSG parsers. In Table 2, lem refers to the LTAG parser (Sarkar et al., 2000), ANSI C implementation of the two-phase parsing algorithm that performs the head corner parsing (van Noord, 1994) without features (phase 1), and then executes feature unification (phase 2). TNT refers to the HPSG parser (Torisawa et al., 2000), C++ implementation of the two-phase parsing algorithm that performs filtering with a compiled CFG (phase 1) and then executes feature unification (phase 2).

Uses

acl_arc_intent_classification:train:1432

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 use of running tallies and percentages is based on the assumption that these features are likely to produce generalized predictors (Litman et al., 1999). confirms, percent-subdials). The DM features also include running tallies for the number of reprompts (num-reprompts), number of confirmation prompts (num-confirms), and number of subdialogue prompts (num-subdials), that had been played up to each point in the dialogue, as well as running percentages (percent-reprompts, percent. Citation Sentence: The use of running tallies and percentages is based on the assumption that these features are likely to produce generalized predictors ( Litman et al. , 1999 ) . Context after the citation: The features obtained via hand-labelling were human transcripts of each user utterance (tscript), a set of semantic labels that are closely related to the system task-type labels (human-label), age (age) and gender (gender) of the user, the actual modality of the user utterance (user-modality) (one of: nothing, speech, touchtone, speech+touchtone, non-speech), and a cleaned transcript with non-word noise information removed (clean-tscript). From these features we calculated two derived features. The first was the number of words in the cleaned transcript (cltscript numwords), again on the assumption that utterance length is strongly correlated with ASR and NLU errors.

Motivation

acl_arc_intent_classification:train:1433

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 Longman lexicographers have developed a grammar coding system capable of representing in compact form a nontrivial amount of information, usually to be found only in large descriptive grammars of English (such as Quirk et al., 1985). LDOCE provides considerably more syntactic information than a traditional dictionary. Figure 4 illustrates the grammar code field for the third word sense of the verb believe as it appears in the published dictionary, on the typesetting tape and after restructuring. Citation Sentence: The Longman lexicographers have developed a grammar coding system capable of representing in compact form a nontrivial amount of information , usually to be found only in large descriptive grammars of English ( such as Quirk et al. , 1985 ) . Context after the citation: A grammar code describes a particular pattern of behaviour of a word. Patterns are descriptive, and are used to convey a range of information: eg. distinctions between count and mass nouns (dog vs. desire), predicative, postpositive and attributive adjectives (asleep vs. elect vs. jokular), noun complementation (fondness, fact) and, most importantly, verb complementation and valency. Grammar codes typically contain a capital letter, followed by a number and, occasionally, a small letter, for example [T5a] or [V3].

Background

acl_arc_intent_classification:train:1434

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: 19 The paper by Alkuhlani and Habash (2012) presents additional, more sophisticated models that we do not use in this article. 18 In this article, we use a newer version of the corpus by Alkuhlani and Habash (2011) than the one we used in Marton, Habash, and Rambow (2011). 17 We also applied the manipulations described in Section A.3 to FNNUM, giving us the variants FNNUMDGT and FNNUMDGTBIN, which we tested similarly. Citation Sentence: 19 The paper by Alkuhlani and Habash ( 2012 ) presents additional , more sophisticated models that we do not use in this article . Context after the citation: shrunk with these functional feature combinations to 0.3%. We take it as further support to the relevance of our functional morphology features, and their partial redundancy with the form-based morphological information embedded in the CATIBEX POS tags.

CompareOrContrast

acl_arc_intent_classification:train:1435

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: 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). The key for quality improvement seems to be rooted mainly in the presence or absence of some form of dictionary. 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: 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 ) . Context after the citation: 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. We proposed here a dictionary-based approach in which morphologically complex word forms, no matter whether they appear in queries or in documents, are segmented into relevant subwords and these subwords are subsequently submitted to the matching procedure. This way, the impact of word form alterations can be eliminated from the retrieval procedure.

Background

acl_arc_intent_classification:train:1436

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: Although there are other discussions of the paragraph as a central element of discourse (e.g. Chafe 1979, Halliday and Hasan 1976, Longacre 1979, Haberlandt et al. 1980), all of them share a certain limitation in their formal techniques for analyzing paragraph structure. Citation Sentence: Although there are other discussions of the paragraph as a central element of discourse ( e.g. Chafe 1979 , Halliday and Hasan 1976 , Longacre 1979 , Haberlandt et al. 1980 ) , all of them share a certain limitation in their formal techniques for analyzing paragraph structure . Context after the citation: Discourse linguists show little interest in making the structural descriptions precise enough so that a computational grammar of text could adapt them and use them. Our interest, however, lies precisely in that area. We suggest that the paragraph is a grammatical and logical unit. It is the smallest linguistic representation of what, in logic, is called a &quot;model,&quot; and it is the first reasonable domain of anaphora resolution, and of coherent thought about a central topic.

CompareOrContrast

acl_arc_intent_classification:train:1437

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: (Details of how the average-expert model performs can be found in our prior work (Xiong and Litman, 2011).) Note that the focus of this paper is comparing feature utilities in different helpfulness models rather than predicting those types of helpfulness ratings. To compare the feature selection results, we examine the four kind of helpfulness models for each of the three feature sets separately, as presented below. Citation Sentence: ( Details of how the average-expert model performs can be found in our prior work ( Xiong and Litman , 2011 ) . ) Context after the citation:

Extends

acl_arc_intent_classification:train:1438

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: 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. 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. Citation Sentence: 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 . Context after the citation: 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:1439

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: Some works abstract perception via the usage of symbolic logic representations (Chen et al., 2010; Chen and Mooney, 2011; Matuszek et al., 2012; Artzi and Zettlemoyer, 2013), while others choose to employ concepts elicited from psycholinguistic and cognition studies. Many approaches to multimodal research have succeeded by abstracting away raw perceptual information and using high-level representations instead. The language grounding problem has received significant attention in recent years, owed in part to the wide availability of data sets (e.g. Flickr, Von Ahn (2006)), computing power, improved computer vision models (Oliva and Torralba, 2001; Lowe, 2004; Farhadi et al., 2009; Parikh and Grauman, 2011) and neurological evidence of ties between the language, perceptual and motor systems in the brain (Pulverm¨uller et al., 2005; Tettamanti et al., 2005; Aziz-Zadeh et al., 2006). Citation Sentence: Some works abstract perception via the usage of symbolic logic representations ( Chen et al. , 2010 ; Chen and Mooney , 2011 ; Matuszek et al. , 2012 ; Artzi and Zettlemoyer , 2013 ) , while others choose to employ concepts elicited from psycholinguistic and cognition studies . Context after the citation: Within the latter category, the two most common representations have been association norms, where subjects are given a 1http://stephenroller.com/research/ emnlp13 cue word and name the first (or several) associated words that come to mind (e.g., Nelson et al. (2004)), and feature norms, where subjects are given a cue word and asked to describe typical properties of the cue concept (e.g., McRae et al. (2005)). Griffiths et al. (2007) helped pave the path for cognitive-linguistic multimodal research, showing that Latent Dirichlet Allocation outperformed Latent Semantic Analysis (Deerwester et al., 1990) in the prediction of association norms.

Background

acl_arc_intent_classification:train:144

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 introduce here a clearly defined and replicable split of the ACE 2004 data, so that future investigations can accurately and correctly compare against the results presented here. The system is trained on the Arabic ACE 2003 and part of the 2004 data. Citation Sentence: We introduce here a clearly defined and replicable split of the ACE 2004 data , so that future investigations can accurately and correctly compare against the results presented here . Context after the citation: There are 689 Arabic documents in LDC's 2004 release (version 1.4) of ACE data from three sources: the Arabic Treebank, a subset of the broadcast (bnews) and newswire (nwire) TDT-4 documents. The 178-document devtest is created by taking the last (in chronological order) 25% of documents in each of three sources: 38 Arabic treebank documents dating from “20000715” (i.e., July 15, 2000) to “20000815,” 76 bnews documents from “20001205.1100.0489” (i.e., Dec. 05 of 2000 from 11:00pm to 04:89am) to “20001230.1100.1216,” and 64 nwire documents from “20001206.1000.0050” to “20001230.0700.0061.” The time span of the test set is intentionally non-overlapping with that of the training set within each data source, as this models how the system will perform in the real world.

Uses

acl_arc_intent_classification:train:1440

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 most cases, the accuracy of parsers degrades when run on out-of-domain data (Gildea, 2001; McClosky et al., 2006; Blitzer et al., 2006; Petrov et al., 2010). This includes work on question answering (Wang et al., 2007), sentiment analysis (Nakagawa et al., 2010), MT reordering (Xu et al., 2009), and many other tasks. The accuracy and speed of state-of-the-art dependency parsers has motivated a resumed interest in utilizing the output of parsing as an input to many downstream natural language processing tasks. Citation Sentence: In most cases , the accuracy of parsers degrades when run on out-of-domain data ( Gildea , 2001 ; McClosky et al. , 2006 ; Blitzer et al. , 2006 ; Petrov et al. , 2010 ) . Context after the citation: But these accuracies are measured with respect to gold-standard out-of-domain parse trees. There are few tasks that actually depend on the complete parse tree. Furthermore, when evaluated on a downstream task, often the optimal parse output has a model score lower than the best parse as predicted by the parsing model. While this means that we are not properly modeling the downstream task in the parsers, it also means that there is some information from small task or domain-specific data sets which could help direct our search for optimal parameters during parser training.

Background

acl_arc_intent_classification:train:1441

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: Brent (1993) relies on morphosyntactic cues in the untagged Brown corpus as indicators of six predefined subcategorization frames. Typically in the approaches based on raw text, a number of subcategorization patterns are predefined, a set of verb subcategorization frame associations are hypothesized from the data, and statistical methods are applied to reliably select hypotheses for the final lexicon. We will divide more-general approaches to subcategorization frame acquisition into two groups: those which extract information from raw text and those which use preparsed and hand-corrected treebank data as their input. Citation Sentence: Brent ( 1993 ) relies on morphosyntactic cues in the untagged Brown corpus as indicators of six predefined subcategorization frames . Context after the citation: The frames do not include details of specific prepositions. Brent used hypothesis testing on binomial frequency data to statistically filter the induced frames. Ushioda et al. (1993) run a finite-state NP parser on a POS-tagged corpus to calculate the relative frequency of the same six subcategorization verb classes. The experiment is limited by the fact that all prepositional phrases are treated as adjuncts.

Background

acl_arc_intent_classification:train:1442

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 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). We then obtain the composed rules by composing two or three adjacent minimal rules. 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. Citation Sentence: 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 ) . Context after the citation: 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. For the U-trees, we run the Gibbs sampler for 1000 iterations on the whole corpus. The sampler uses 1,087s per iteration, on average, using a single core, 2.3 GHz Intel Xeon machine. For the hyperparameters, we set Ä® to 0.1 and pexpmrd = 1/3 to give a preference to the rules with small fragments.

Uses

acl_arc_intent_classification:train:1443

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: One obvious approach to this problem is to employ parser reranking (Collins, 2000). The goal being not necessarily to obtain better parse performance, but to exploit the structure induced from human labeled treebank data while targeting specific extrinsic metrics of quality, which can include task specific metrics or external weak constraints on the parse structure. While this means that we are not properly modeling the downstream task in the parsers, it also means that there is some information from small task or domain-specific data sets which could help direct our search for optimal parameters during parser training. Citation Sentence: One obvious approach to this problem is to employ parser reranking ( Collins , 2000 ) . Context after the citation: In such a setting, an auxiliary reranker is added in a pipeline following the parser. The standard setting involves training the base parser and applying it to a development set (this is often done in a cross-validated jack-knife training framework). The reranker can then be trained to optimize for the downstream or extrinsic objective. While this will bias the reranker towards the target task, it is limited by the oracle performance of the original base parser.

Background

acl_arc_intent_classification:train:1444

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: Ideally, to distinguish between raising and equi verbs, a number of syntactic criteria should be employed (Perlmutter and Soames, 1979:460ff.) Thirdly, it seems clear that the Object Raising rule is straining the limits of what can be reliably extracted from the LDOCE coding system. source of error in the case of the Object Raising rule. Citation Sentence: Ideally , to distinguish between raising and equi verbs , a number of syntactic criteria should be employed ( Perlmutter and Soames , 1979:460 ff . ) Context after the citation: . However, only two of these criteria are explicit in the coding system. On the basis of the results obtained, we explored the possibility of modifying the Object Raising rule to take account of the cooccurrence of T5 and T5a codes and V or X codes within a homograph, rather than within a word sense. An exhaustive search of the dictionary produced 24 verbs coded in this fashion.

FutureWork

acl_arc_intent_classification:train:1445

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 table also presents the closest comparable experimental results reported by McKnight and Srinivasan (2003).1 McKnight and Srinivasan (henceforth, M&S) created a test collection consisting of 37,151 RCTs from approximately 12 million MEDLINE abstracts dated between 1976 and 2001. Table 2(b) reports accuracy, precision, recall, and Fmeasure for four separate binary classifiers (HMM with LDA) specifically trained for each of the sections (one per row in the table). Table 2(a) reports the multi-way classification error rate; once again, applying the Markov assumption to model discourse transitions improves performance, and using LDA further reduces error rate. Citation Sentence: The table also presents the closest comparable experimental results reported by McKnight and Srinivasan ( 2003 ) .1 McKnight and Srinivasan ( henceforth , M&S ) created a test collection consisting of 37,151 RCTs from approximately 12 million MEDLINE abstracts dated between 1976 and 2001 . Context after the citation: This collection has 1After contacting the authors, we were unable to obtain the same exact dataset that they used for their experiments. significantly more training examples than our corpus of 27,075 abstracts, which could be a source of performance differences. Furthermore, details regarding their procedure for mapping structured abstract headings to one of the four general labels was not discussed in their paper.

CompareOrContrast

acl_arc_intent_classification:train:1446

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: How this mismatched perceptual basis affects referential communication in situated dialogue was investigated in our previous work (Liu et al., 2012). Thus, referential communication between the human and the robot becomes difficult. Although physically co-present, a joint perceptual basis between the human and the robot cannot be established (Clark and Brennan, 1991). Citation Sentence: How this mismatched perceptual basis affects referential communication in situated dialogue was investigated in our previous work ( Liu et al. , 2012 ) . Context after the citation: In that work, the main focus is on reference resolution: given referential descriptions from human partners, how to identify referents in the environment even though the robot only has imperfect perception of the environment. Since robots need to collaborate with human partners to establish a joint perceptual basis, referring expression generation (REG) becomes an equally important problem in situated dialogue. Robots have much lower perceptual capabilities of the environment than humans. How can a robot effectively generate referential descriptions about the environment so that its human partner can understand which objects are being referred to?

Extends

acl_arc_intent_classification:train:1447