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Improving Semantic Parsing via Answer Type Inference | Yavuz, Semih and
Gur, Izzeddin and
Su, Yu and
Srivatsa, Mudhakar and
Yan, Xifeng | 2,016 | nan | 149--159 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | f3594f9d60c98cac88f9033c69c2b666713ed6d6 | 1 |
Automatic Extraction of Implicit Interpretations from Modal Constructions | Sanders, Jordan and
Blanco, Eduardo | 2,016 | nan | 1098--1107 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 813779418a613d1faecd7b1deb9b4456121a9b7e | 0 |
Factorizing Complex Models: A Case Study in Mention Detection | Florian, Radu and
Jing, Hongyan and
Kambhatla, Nanda and
Zitouni, Imed | 2,006 | nan | 473--480 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 3221a2c32488439c61bcfad832c50917e1ef3bdf | 1 |
Translation Context Sensitive {WSD} | Specia, Lucia and
das Gra{\c{c}}as Volpe Nunes, Maria and
Stevenson, Mark | 2,006 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | cffc77dc35c179166bb37124a759585cbcfd5d8a | 0 |
End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification | Karn, Sanjeev and
Waltinger, Ulli and
Sch{\"u}tze, Hinrich | 2,017 | We address fine-grained entity classification and propose a novel attention-based recurrent neural network (RNN) encoder-decoder that generates paths in the type hierarchy and can be trained end-to-end. We show that our model performs better on fine-grained entity classification than prior work that relies on flat or local classifiers that do not directly model hierarchical structure. | 752--758 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 7615ed4f35f84cc086b9ae8e421891f3d33c68a6 | 1 |
Using Gaze Data to Predict Multiword Expressions | Rohanian, Omid and
Taslimipoor, Shiva and
Yaneva, Victoria and
Ha, Le An | 2,017 | In recent years gaze data has been increasingly used to improve and evaluate NLP models due to the fact that it carries information about the cognitive processing of linguistic phenomena. In this paper we conduct a preliminary study towards the automatic identification of multiword expressions based on gaze features from native and non-native speakers of English. We report comparisons between a part-of-speech (POS) and frequency baseline to: i) a prediction model based solely on gaze data and ii) a combined model of gaze data, POS and frequency. In spite of the challenging nature of the task, best performance was achieved by the latter. Furthermore, we explore how the type of gaze data (from native versus non-native speakers) affects the prediction, showing that data from the two groups is discriminative to an equal degree for the task. Finally, we show that late processing measures are more predictive than early ones, which is in line with previous research on idioms and other formulaic structures. | 601--609 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 6cbb05739c8d033eb027222c1a395391d877bc61 | 0 |
A Maximum Entropy Approach to Natural Language Processing | Berger, Adam L. and
Della Pietra, Stephen A. and
Della Pietra, Vincent J. | 1,996 | nan | 39--71 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | fb486e03369a64de2d5b0df86ec0a7b55d3907db | 1 |
Extracting Topics from Texts Based on Situations | Ma, Zhiyi and
Zhan, Xuegong and
Yao, Tianshun | 1,996 | nan | 357--362 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | f8c4d4bd5f119e1d29e7fa38e81de6316817bda3 | 0 |
Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases | Jin, Hailong and
Hou, Lei and
Li, Juanzi and
Dong, Tiansi | 2,018 | Fine-grained entity typing aims at identifying the semantic type of an entity in KB. Type information is very important in knowledge bases, but are unfortunately incomplete even in some large knowledge bases. Limitations of existing methods are either ignoring the structure and type information in KB or requiring large scale annotated corpus. To address these issues, we propose an attributed and predictive entity embedding method, which can fully utilize various kinds of information comprehensively. Extensive experiments on two real DBpedia datasets show that our proposed method significantly outperforms 8 state-of-the-art methods, with 4.0{\%} and 5.2{\%} improvement in Mi-F1 and Ma-F1, respectively. | 282--292 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | fc958f9f9876689acbe99ad80c508ed7ad0e40bf | 1 |
{GKR}: the Graphical Knowledge Representation for semantic parsing | Kalouli, Aikaterini-Lida and
Crouch, Richard | 2,018 | This paper describes the first version of an open-source semantic parser that creates graphical representations of sentences to be used for further semantic processing, e.g. for natural language inference, reasoning and semantic similarity. The Graphical Knowledge Representation which is output by the parser is inspired by the Abstract Knowledge Representation, which separates out conceptual and contextual levels of representation that deal respectively with the subject matter of a sentence and its existential commitments. Our representation is a layered graph with each sub-graph holding different kinds of information, including one sub-graph for concepts and one for contexts. Our first evaluation of the system shows an F-score of 85{\%} in accurately representing sentences as semantic graphs. | 27--37 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 0e195eb08cb94a2aedadae06442ee2d4e0cc1016 | 0 |
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing | Xiong, Wenhan and
Wu, Jiawei and
Lei, Deren and
Yu, Mo and
Chang, Shiyu and
Guo, Xiaoxiao and
Wang, William Yang | 2,019 | Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance. Such techniques, however, are not directly applicable to more open and practical scenarios where the type set is not restricted by KB schema and includes a vast number of free-form types. To model the underlying label correlations without access to manually annotated label structures, we introduce a novel label-relational inductive bias, represented by a graph propagation layer that effectively encodes both global label co-occurrence statistics and word-level similarities. On a large dataset with over 10,000 free-form types, the graph-enhanced model equipped with an attention-based matching module is able to achieve a much higher recall score while maintaining a high-level precision. Specifically, it achieves a 15.3{\%} relative F1 improvement and also less inconsistency in the outputs. We further show that a simple modification of our proposed graph layer can also improve the performance on a conventional and widely-tested dataset that only includes KB-schema types. | 773--784 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | a0713d945b2e5c2bdeeba68399c8ac6ea84e0ca6 | 1 |
Sample Size in {A}rabic Authorship Verification | Ahmed, Hossam | 2,019 | nan | 84--91 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | f5f6cdf8deb778abc8469baed6824aeaf28289be | 0 |
The Life and Death of Discourse Entities: Identifying Singleton Mentions | Recasens, Marta and
de Marneffe, Marie-Catherine and
Potts, Christopher | 2,013 | nan | 627--633 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 0fdf52226bde594ca069b2768249a4b10da9255d | 1 |
Annotating Legitimate Disagreement in Corpus Construction | Wong, Billy T.M. and
Lee, Sophia Y.M. | 2,013 | nan | 51--57 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 0e575e0c10cf734b491b7a11b2fd47f5dcd7c33e | 0 |
Coreference in {W}ikipedia: Main Concept Resolution | Ghaddar, Abbas and
Langlais, Phillippe | 2,016 | nan | 229--238 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | da07e8d3951aeda87846f4b7db321576b48a2c60 | 1 |
Comparing Named-Entity Recognizers in a Targeted Domain: Handcrafted Rules vs Machine Learning | Partalas, Ioannis and
Lopez, C{\'e}dric and
Segond, Fr{\'e}d{\'e}rique | 2,016 | Comparing Named-Entity Recognizers in a Targeted Domain : Handcrafted Rules vs. Machine Learning Named-Entity Recognition concerns the classification of textual objects in a predefined set of categories such as persons, organizations, and localizations. While Named-Entity Recognition is well studied since 20 years, the application to specialized domains still poses challenges for current systems. We developed a rule-based system and two machine learning approaches to tackle the same task : recognition of product names, brand names, etc., in the domain of Cosmetics, for French. Our systems can thus be compared under ideal conditions. In this paper, we introduce both systems and we compare them. | 389--395 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | f64f17a5be7e9a5e05e5a9fce1c01058ba024d1d | 0 |
Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks | Das, Rajarshi and
Zaheer, Manzil and
Reddy, Siva and
McCallum, Andrew | 2,017 | Existing question answering methods infer answers either from a knowledge base or from raw text. While knowledge base (KB) methods are good at answering compositional questions, their performance is often affected by the incompleteness of the KB. Au contraire, web text contains millions of facts that are absent in the KB, however in an unstructured form. Universal schema can support reasoning on the union of both structured KBs and unstructured text by aligning them in a common embedded space. In this paper we extend universal schema to natural language question answering, employing Memory networks to attend to the large body of facts in the combination of text and KB. Our models can be trained in an end-to-end fashion on question-answer pairs. Evaluation results on Spades fill-in-the-blank question answering dataset show that exploiting universal schema for question answering is better than using either a KB or text alone. This model also outperforms the current state-of-the-art by 8.5 F1 points. | 358--365 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 2b2090eab4abe27e6e5e4ca94afaf82e511b63bd | 1 |
Applying {BLAST} to Text Reuse Detection in {F}innish Newspapers and Journals, 1771-1910 | Vesanto, Aleksi and
Nivala, Asko and
Rantala, Heli and
Salakoski, Tapio and
Salmi, Hannu and
Ginter, Filip | 2,017 | nan | 54--58 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | f69a2cf20e8a80cc9da747e2273f230b39847515 | 0 |
Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition | Kato, Takuma and
Abe, Kaori and
Ouchi, Hiroki and
Miyawaki, Shumpei and
Suzuki, Jun and
Inui, Kentaro | 2,020 | In general, the labels used in sequence labeling consist of different types of elements. For example, IOB-format entity labels, such as B-Person and I-Person, can be decomposed into span (B and I) and type information (Person). However, while most sequence labeling models do not consider such label components, the shared components across labels, such as Person, can be beneficial for label prediction. In this work, we propose to integrate label component information as embeddings into models. Through experiments on English and Japanese fine-grained named entity recognition, we demonstrate that the proposed method improves performance, especially for instances with low-frequency labels. | 222--229 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 926588efc881ba9f4322ddfaae14555d058bbb46 | 1 |
Autoencoding Keyword Correlation Graph for Document Clustering | Chiu, Billy and
Sahu, Sunil Kumar and
Thomas, Derek and
Sengupta, Neha and
Mahdy, Mohammady | 2,020 | Document clustering requires a deep understanding of the complex structure of long-text; in particular, the intra-sentential (local) and inter-sentential features (global). Existing representation learning models do not fully capture these features. To address this, we present a novel graph-based representation for document clustering that builds a \textit{graph autoencoder} (GAE) on a Keyword Correlation Graph. The graph is constructed with topical keywords as nodes and multiple local and global features as edges. A GAE is employed to aggregate the two sets of features by learning a latent representation which can jointly reconstruct them. Clustering is then performed on the learned representations, using vector dimensions as features for inducing document classes. Extensive experiments on two datasets show that the features learned by our approach can achieve better clustering performance than other existing features, including term frequency-inverse document frequency and average embedding. | 3974--3981 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | e78b1c6cf0fbe935f12adf3a5ce3cde629252316 | 0 |
A Joint Named Entity Recognition and Entity Linking System | Stern, Rosa and
Sagot, Beno{\^\i}t and
B{\'e}chet, Fr{\'e}d{\'e}ric | 2,012 | nan | 52--60 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 443e814ab3b87ea51a18d4a3925a0fadeca03a9a | 1 |
Regular polysemy: A distributional model | Boleda, Gemma and
Pad{\'o}, Sebastian and
Utt, Jason | 2,012 | nan | 151--160 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 969fdeafe142994f4bf41ffc35ffea235de2aa18 | 0 |
Mining Entity Types from Query Logs via User Intent Modeling | Pantel, Patrick and
Lin, Thomas and
Gamon, Michael | 2,012 | nan | 563--571 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | c19b48e088983bf3ab71751000d78409293f4cf0 | 1 |
Nouvelle approche pour le regroupement des locuteurs dans des {\'e}missions radiophoniques et t{\'e}l{\'e}visuelles (New approach for speaker clustering of broadcast news) [in {F}rench] | Rouvier, Mickael and
Meignier, Sylvain | 2,012 | nan | 97--104 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | ba643220c830a2db0b0d0e0aa3d556bc30d40b2b | 0 |
Label Embedding for Zero-shot Fine-grained Named Entity Typing | Ma, Yukun and
Cambria, Erik and
Gao, Sa | 2,016 | Named entity typing is the task of detecting the types of a named entity in context. For instance, given {``}Eric is giving a presentation{''}, our goal is to infer that {`}Eric{'} is a speaker or a presenter and a person. Existing approaches to named entity typing cannot work with a growing type set and fails to recognize entity mentions of unseen types. In this paper, we present a label embedding method that incorporates prototypical and hierarchical information to learn pre-trained label embeddings. In addition, we adapt a zero-shot learning framework that can predict both seen and previously unseen entity types. We perform evaluation on three benchmark datasets with two settings: 1) few-shots recognition where all types are covered by the training set; and 2) zero-shot recognition where fine-grained types are assumed absent from training set. Results show that prior knowledge encoded using our label embedding methods can significantly boost the performance of classification for both cases. | 171--180 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 0d12035f96d795fef0d6b4f70340934dd3dd98a1 | 1 |
{ELMD}: An Automatically Generated Entity Linking Gold Standard Dataset in the Music Domain | Oramas, Sergio and
Anke, Luis Espinosa and
Sordo, Mohamed and
Saggion, Horacio and
Serra, Xavier | 2,016 | In this paper we present a gold standard dataset for Entity Linking (EL) in the Music Domain. It contains thousands of musical named entities such as Artist, Song or Record Label, which have been automatically annotated on a set of artist biographies coming from the Music website and social network Last.fm. The annotation process relies on the analysis of the hyperlinks present in the source texts and in a voting-based algorithm for EL, which considers, for each entity mention in text, the degree of agreement across three state-of-the-art EL systems. Manual evaluation shows that EL Precision is at least 94{\%}, and due to its tunable nature, it is possible to derive annotations favouring higher Precision or Recall, at will. We make available the annotated dataset along with evaluation data and the code. | 3312--3317 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | a529e05211d685bbc6f80f1081e4784c325ea8d0 | 0 |
Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks | Jin, Hailong and
Hou, Lei and
Li, Juanzi and
Dong, Tiansi | 2,019 | This paper addresses the problem of inferring the fine-grained type of an entity from a knowledge base. We convert this problem into the task of graph-based semi-supervised classification, and propose Hierarchical Multi Graph Convolutional Network (HMGCN), a novel Deep Learning architecture to tackle this problem. We construct three kinds of connectivity matrices to capture different kinds of semantic correlations between entities. A recursive regularization is proposed to model the subClassOf relations between types in given type hierarchy. Extensive experiments with two large-scale public datasets show that our proposed method significantly outperforms four state-of-the-art methods. | 4969--4978 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 074e3497b03366caf2e17acd59fb1c52ccf8be55 | 1 |
Multi-grained Attention with Object-level Grounding for Visual Question Answering | Huang, Pingping and
Huang, Jianhui and
Guo, Yuqing and
Qiao, Min and
Zhu, Yong | 2,019 | Attention mechanisms are widely used in Visual Question Answering (VQA) to search for visual clues related to the question. Most approaches train attention models from a coarse-grained association between sentences and images, which tends to fail on small objects or uncommon concepts. To address this problem, this paper proposes a multi-grained attention method. It learns explicit word-object correspondence by two types of word-level attention complementary to the sentence-image association. Evaluated on the VQA benchmark, the multi-grained attention model achieves competitive performance with state-of-the-art models. And the visualized attention maps demonstrate that addition of object-level groundings leads to a better understanding of the images and locates the attended objects more precisely. | 3595--3600 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 144f4d5dcd0b13935ff0d0890c2ec37aa40039b1 | 0 |
Automatic Acquisition of Hyponyms from Large Text Corpora | Hearst, Marti A. | 1,992 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | dbfd191afbbc8317577cbc44afe7156df546e143 | 1 |
The Acquisition of Lexical Knowledge from Combined Machine-Readable Dictionary Sources | Sanfilippo, Antonio and
Poznatlski, Victor | 1,992 | nan | 80--87 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 0616b0f5e6edce01f153081e53bd0152c8d0a4bd | 0 |
Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition | Lothritz, Cedric and
Allix, Kevin and
Veiber, Lisa and
Bissyand{\'e}, Tegawend{\'e} F. and
Klein, Jacques | 2,020 | Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task and has remained an active research field. In recent years, transformer models and more specifically the BERT model developed at Google revolutionised the field of NLP. While the performance of transformer-based approaches such as BERT has been studied for NER, there has not yet been a study for the fine-grained Named Entity Recognition (FG-NER) task. In this paper, we compare three transformer-based models (BERT, RoBERTa, and XLNet) to two non-transformer-based models (CRF and BiLSTM-CNN-CRF). Furthermore, we apply each model to a multitude of distinct domains. We find that transformer-based models incrementally outperform the studied non-transformer-based models in most domains with respect to the F1 score. Furthermore, we find that the choice of domains significantly influenced the performance regardless of the respective data size or the model chosen. | 3750--3760 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | e25dc08340655401a034a90bf091c1a185c422b6 | 1 |
Cross-lingual Semantic Representation for {NLP} with {UCCA} | Abend, Omri and
Dvir, Dotan and
Hershcovich, Daniel and
Prange, Jakob and
Schneider, Nathan | 2,020 | This is an introductory tutorial to UCCA (Universal Conceptual Cognitive Annotation), a cross-linguistically applicable framework for semantic representation, with corpora annotated in English, German and French, and ongoing annotation in Russian and Hebrew. UCCA builds on extensive typological work and supports rapid annotation. The tutorial will provide a detailed introduction to the UCCA annotation guidelines, design philosophy and the available resources; and a comparison to other meaning representations. It will also survey the existing parsing work, including the findings of three recent shared tasks, in SemEval and CoNLL, that addressed UCCA parsing. Finally, the tutorial will present recent applications and extensions to the scheme, demonstrating its value for natural language processing in a range of languages and domains. | 1--9 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 4ddb820ad3dceeb777967d98fbae6711de5077eb | 0 |
Fine-Grained Entity Typing in Hyperbolic Space | L{\'o}pez, Federico and
Heinzerling, Benjamin and
Strube, Michael | 2,019 | How can we represent hierarchical information present in large type inventories for entity typing? We study the suitability of hyperbolic embeddings to capture hierarchical relations between mentions in context and their target types in a shared vector space. We evaluate on two datasets and propose two different techniques to extract hierarchical information from the type inventory: from an expert-generated ontology and by automatically mining the dataset. The hyperbolic model shows improvements in some but not all cases over its Euclidean counterpart. Our analysis suggests that the adequacy of this geometry depends on the granularity of the type inventory and the representation of its distribution. | 169--180 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | cded59d31ab4841baa1517ff0359f0f2f4b865f5 | 1 |
The Impact of Semantic Linguistic Features in Relation Extraction: A Logical Relational Learning Approach | Lima, Rinaldo and
Espinasse, Bernard and
Freitas, Frederico | 2,019 | Relation Extraction (RE) consists in detecting and classifying semantic relations between entities in a sentence. The vast majority of the state-of-the-art RE systems relies on morphosyntactic features and supervised machine learning algorithms. This paper tries to answer important questions concerning both the impact of semantic based features, and the integration of external linguistic knowledge resources on RE performance. For that, a RE system based on a logical and relational learning algorithm was used and evaluated on three reference datasets from two distinct domains. The yielded results confirm that the classifiers induced using the proposed richer feature set outperformed the classifiers built with morphosyntactic features in average 4{\%} (F1-measure). | 648--654 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 72f07997e910d48a7e6557441c893ddca107c6f8 | 0 |
Automatic construction of a hypernym-labeled noun hierarchy from text | Caraballo, Sharon A. | 1,999 | nan | 120--126 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | aab329ef59d21060c31afce413f6e447b1c0b8b7 | 1 |
Improved Alignment Models for Statistical Machine Translation | Och, Franz Josef and
Tillmann, Christoph and
Ney, Hermann | 1,999 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 8b0495331238da6c0e7be0bfdb9b5453b33c1f98 | 0 |
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking | Murty, Shikhar and
Verga, Patrick and
Vilnis, Luke and
Radovanovic, Irena and
McCallum, Andrew | 2,018 | Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities contained in curated ontologies. Previous attempts to incorporate hierarchical structure have yielded little benefit and are restricted to shallow ontologies. This paper presents new methods using real and complex bilinear mappings for integrating hierarchical information, yielding substantial improvement over flat predictions in entity linking and fine-grained entity typing, and achieving new state-of-the-art results for end-to-end models on the benchmark FIGER dataset. We also present two new human-annotated datasets containing wide and deep hierarchies which we will release to the community to encourage further research in this direction: \textit{MedMentions}, a collection of PubMed abstracts in which 246k mentions have been mapped to the massive UMLS ontology; and \textit{TypeNet}, which aligns Freebase types with the WordNet hierarchy to obtain nearly 2k entity types. In experiments on all three datasets we show substantial gains from hierarchy-aware training. | 97--109 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 35112824817b78156a6b2bcd2a5622a26ee16600 | 1 |
Findings of the {E}2{E} {NLG} Challenge | Du{\v{s}}ek, Ond{\v{r}}ej and
Novikova, Jekaterina and
Rieser, Verena | 2,018 | This paper summarises the experimental setup and results of the first shared task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue systems. Recent end-to-end generation systems are promising since they reduce the need for data annotation. However, they are currently limited to small, delexicalised datasets. The E2E NLG shared task aims to assess whether these novel approaches can generate better-quality output by learning from a dataset containing higher lexical richness, syntactic complexity and diverse discourse phenomena. We compare 62 systems submitted by 17 institutions, covering a wide range of approaches, including machine learning architectures {--} with the majority implementing sequence-to-sequence models (seq2seq) {--} as well as systems based on grammatical rules and templates. | 322--328 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 92cfd6d2eb957805aaf4786dacb484081a469e80 | 0 |
No Noun Phrase Left Behind: Detecting and Typing Unlinkable Entities | Lin, Thomas and
{Mausam} and
Etzioni, Oren | 2,012 | nan | 893--903 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | cffb556ee3d1e188f4688b71a8608bbe1883bc49 | 1 |
Language Richness of the Web | Majli{\v{s}}, Martin and
{\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k | 2,012 | We have built a corpus containing texts in 106 languages from texts available on the Internet and on Wikipedia. The W2C Web Corpus contains 54.7{\textasciitilde}GB of text and the W2C Wiki Corpus contains 8.5{\textasciitilde}GB of text. The W2C Web Corpus contains more than 100{\textasciitilde}MB of text available for 75 languages. At least 10{\textasciitilde}MB of text is available for 100 languages. These corpora are a unique data source for linguists, since they outclass all published works both in the size of the material collected and the number of languages covered. This language data resource can be of use particularly to researchers specialized in multilingual technologies development. We also developed software that greatly simplifies the creation of a new text corpus for a given language, using text materials freely available on the Internet. Special attention was given to components for filtering and de-duplication that allow to keep the material quality very high. | 2927--2934 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 33e7d35324cd138730022db297c358fc66149160 | 0 |
Collective Entity Resolution with Multi-Focal Attention | Globerson, Amir and
Lazic, Nevena and
Chakrabarti, Soumen and
Subramanya, Amarnag and
Ringgaard, Michael and
Pereira, Fernando | 2,016 | nan | 621--631 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 4988a269e9f61c6fd1da502e34648b93dfd1a54d | 1 |
Results of the 4th edition of {B}io{ASQ} Challenge | Krithara, Anastasia and
Nentidis, Anastasios and
Paliouras, Georgios and
Kakadiaris, Ioannis | 2,016 | nan | 1--7 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 2d5108706bfd88506c27adceb87ce46e75f9cad2 | 0 |
The Automatic Content Extraction ({ACE}) Program {--} Tasks, Data, and Evaluation | Doddington, George and
Mitchell, Alexis and
Przybocki, Mark and
Ramshaw, Lance and
Strassel, Stephanie and
Weischedel, Ralph | 2,004 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 0617dd6924df7a3491c299772b70e90507b195dc | 1 |
Generating Paired Transliterated-cognates Using Multiple Pronunciation Characteristics from Web corpora | Kuo, Jin-Shea and
Yang, Ying-Kuei | 2,004 | nan | 275--282 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | adfbf15a0059f68af95c2836b890577807d66550 | 0 |
Transforming {W}ikipedia into Named Entity Training Data | Nothman, Joel and
Curran, James R. and
Murphy, Tara | 2,008 | nan | 124--132 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 04ca48e573c0800fc572f2af1d475dd2645e840a | 1 |
Automatic Extraction of Briefing Templates | Das, Dipanjan and
Kumar, Mohit and
Rudnicky, Alexander I. | 2,008 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | e1dfe8ad1f4cfb45484118d3faf0f13505e483e1 | 0 |
{AFET}: Automatic Fine-Grained Entity Typing by Hierarchical Partial-Label Embedding | Ren, Xiang and
He, Wenqi and
Qu, Meng and
Huang, Lifu and
Ji, Heng and
Han, Jiawei | 2,016 | nan | 1369--1378 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | ee42c6c3c5db2f0eb40faacf6e3b80035a645287 | 1 |
Understanding Discourse on Work and Job-Related Well-Being in Public Social Media | Liu, Tong and
Homan, Christopher and
Ovesdotter Alm, Cecilia and
Lytle, Megan and
Marie White, Ann and
Kautz, Henry | 2,016 | nan | 1044--1053 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | e32666501e824d1cfdc19534b0ed009c7268cd8a | 0 |
{J}-{NERD}: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features | Nguyen, Dat Ba and
Theobald, Martin and
Weikum, Gerhard | 2,016 | Methods for Named Entity Recognition and Disambiguation (NERD) perform NER and NED in two separate stages. Therefore, NED may be penalized with respect to precision by NER false positives, and suffers in recall from NER false negatives. Conversely, NED does not fully exploit information computed by NER such as types of mentions. This paper presents J-NERD, a new approach to perform NER and NED jointly, by means of a probabilistic graphical model that captures mention spans, mention types, and the mapping of mentions to entities in a knowledge base. We present experiments with different kinds of texts from the CoNLL{'}03, ACE{'}05, and ClueWeb{'}09-FACC1 corpora. J-NERD consistently outperforms state-of-the-art competitors in end-to-end NERD precision, recall, and F1. | 215--229 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 181e0f95a61d769e01f6d2c520d60d4df228c5c0 | 1 |
{F}rench Learners Audio Corpus of {G}erman Speech ({FLACGS}) | Wottawa, Jane and
Adda-Decker, Martine | 2,016 | The French Learners Audio Corpus of German Speech (FLACGS) was created to compare German speech production of German native speakers (GG) and French learners of German (FG) across three speech production tasks of increasing production complexity: repetition, reading and picture description. 40 speakers, 20 GG and 20 FG performed each of the three tasks, which in total leads to approximately 7h of speech. The corpus was manually transcribed and automatically aligned. Analysis that can be performed on this type of corpus are for instance segmental differences in the speech production of L2 learners compared to native speakers. We chose the realization of the velar nasal consonant engma. In spoken French, engma does not appear in a VCV context which leads to production difficulties in FG. With increasing speech production complexity (reading and picture description), engma is realized as engma + plosive by FG in over 50{\%} of the cases. The results of a two way ANOVA with unequal sample sizes on the durations of the different realizations of engma indicate that duration is a reliable factor to distinguish between engma and engma + plosive in FG productions compared to the engma productions in GG in a VCV context. The FLACGS corpus allows to study L2 production and perception. | 3215--3219 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | e5a5dd2a6b9d3aa69f57a7180fd129b338d82866 | 0 |
Entity Linking via Joint Encoding of Types, Descriptions, and Context | Gupta, Nitish and
Singh, Sameer and
Roth, Dan | 2,017 | For accurate entity linking, we need to capture various information aspects of an entity, such as its description in a KB, contexts in which it is mentioned, and structured knowledge. Additionally, a linking system should work on texts from different domains without requiring domain-specific training data or hand-engineered features. In this work we present a neural, modular entity linking system that learns a unified dense representation for each entity using multiple sources of information, such as its description, contexts around its mentions, and its fine-grained types. We show that the resulting entity linking system is effective at combining these sources, and performs competitively, sometimes out-performing current state-of-the-art systems across datasets, without requiring any domain-specific training data or hand-engineered features. We also show that our model can effectively {``}embed{''} entities that are new to the KB, and is able to link its mentions accurately. | 2681--2690 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 2927dfc481446568fc9108795570eb4d416be021 | 1 |
Efficient Attention using a Fixed-Size Memory Representation | Britz, Denny and
Guan, Melody and
Luong, Minh-Thang | 2,017 | The standard content-based attention mechanism typically used in sequence-to-sequence models is computationally expensive as it requires the comparison of large encoder and decoder states at each time step. In this work, we propose an alternative attention mechanism based on a fixed size memory representation that is more efficient. Our technique predicts a compact set of K attention contexts during encoding and lets the decoder compute an efficient lookup that does not need to consult the memory. We show that our approach performs on-par with the standard attention mechanism while yielding inference speedups of 20{\%} for real-world translation tasks and more for tasks with longer sequences. By visualizing attention scores we demonstrate that our models learn distinct, meaningful alignments. | 392--400 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 87fc28cbb193a3bc100e13a4a57a8dc9ce7e31a3 | 0 |
Structured Generative Models for Unsupervised Named-Entity Clustering | Elsner, Micha and
Charniak, Eugene and
Johnson, Mark | 2,009 | nan | 164--172 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | dfedad21048cafdb7066cd2caeba13228e83d4eb | 1 |
Fertility-based Source-Language-biased Inversion Transduction Grammar for Word Alignment | Huang, Chung-Chi and
Chang, Jason S. | 2,009 | nan | 1--18 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 498b24e27e2a2cec829afbb1163cd456a01ba668 | 0 |
Instance-Based Ontology Population Exploiting Named-Entity Substitution | Giuliano, Claudio and
Gliozzo, Alfio | 2,008 | nan | 265--272 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | aa480756d7ecee36e500ed05e13d0eb3bfe0aa2d | 1 |
The {M}ove{O}n Motorcycle Speech Corpus | Winkler, Thomas and
Kostoulas, Theodoros and
Adderley, Richard and
Bonkowski, Christian and
Ganchev, Todor and
K{\"o}hler, Joachim and
Fakotakis, Nikos | 2,008 | A speech and noise corpus dealing with the extreme conditions of the motorcycle environment is developed within the MoveOn project. Speech utterances in British English are recorded and processed approaching the issue of command and control and template driven dialog systems on the motorcycle. The major part of the corpus comprises noisy speech and environmental noise recorded on a motorcycle, but several clean speech recordings in a silent environment are also available. The corpus development focuses on distortion free recordings and accurate descriptions of both recorded speech and noise. Not only speech segments are annotated but also annotation of environmental noise is performed. The corpus is a small-sized speech corpus with about 12 hours of clean and noisy speech utterances and about 30 hours of segments with environmental noise without speech. This paper addresses the motivation and development of the speech corpus and finally presents some statistics and results of the database creation. | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | beae598d40e025802505ef076d95e8d3f2ca2d3c | 0 |
Semantic Class Learning from the Web with Hyponym Pattern Linkage Graphs | Kozareva, Zornitsa and
Riloff, Ellen and
Hovy, Eduard | 2,008 | nan | 1048--1056 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 194587b9d80e29aa6e50e9b0c628b581b66ea364 | 1 |
Machine Translation for {I}ndonesian and {T}agalog | Laugher, Brianna and
MacLeod, Ben | 2,008 | Kataku is a hybrid MT system for Indonesian to English and English to Indonesian translation, available on Windows, Linux and web-based platforms. This paper briefly presents the technical background to Kataku, some of its use cases and extensions. Kataku is the flagship product of ToggleText, a language technology company based in Melbourne, Australia. | 397--401 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 5ab505f24d2b3f5391ed64b1dbf6796411f6f1fd | 0 |
{S}cience{E}xam{CER}: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition | Smith, Hannah and
Zhang, Zeyu and
Culnan, John and
Jansen, Peter | 2,020 | Named entity recognition identifies common classes of entities in text, but these entity labels are generally sparse, limiting utility to downstream tasks. In this work we present ScienceExamCER, a densely-labeled semantic classification corpus of 133k mentions in the science exam domain where nearly all (96{\%}) of content words have been annotated with one or more fine-grained semantic class labels including taxonomic groups, meronym groups, verb/action groups, properties and values, and synonyms. Semantic class labels are drawn from a manually-constructed fine-grained typology of 601 classes generated through a data-driven analysis of 4,239 science exam questions. We show an off-the-shelf BERT-based named entity recognition model modified for multi-label classification achieves an accuracy of 0.85 F1 on this task, suggesting strong utility for downstream tasks in science domain question answering requiring densely-labeled semantic classification. | 4529--4546 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | e9a679c4215762478f1b849101b09102ed39c6b1 | 1 |
Exclusive Hierarchical Decoding for Deep Keyphrase Generation | Chen, Wang and
Chan, Hou Pong and
Li, Piji and
King, Irwin | 2,020 | Keyphrase generation (KG) aims to summarize the main ideas of a document into a set of keyphrases. A new setting is recently introduced into this problem, in which, given a document, the model needs to predict a set of keyphrases and simultaneously determine the appropriate number of keyphrases to produce. Previous work in this setting employs a sequential decoding process to generate keyphrases. However, such a decoding method ignores the intrinsic hierarchical compositionality existing in the keyphrase set of a document. Moreover, previous work tends to generate duplicated keyphrases, which wastes time and computing resources. To overcome these limitations, we propose an exclusive hierarchical decoding framework that includes a hierarchical decoding process and either a soft or a hard exclusion mechanism. The hierarchical decoding process is to explicitly model the hierarchical compositionality of a keyphrase set. Both the soft and the hard exclusion mechanisms keep track of previously-predicted keyphrases within a window size to enhance the diversity of the generated keyphrases. Extensive experiments on multiple KG benchmark datasets demonstrate the effectiveness of our method to generate less duplicated and more accurate keyphrases. | 1095--1105 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | ba46ece6feba34c408d081a8dce66f0ecf4b7a60 | 0 |
Two/Too Simple Adaptations of {W}ord2{V}ec for Syntax Problems | Ling, Wang and
Dyer, Chris and
Black, Alan W. and
Trancoso, Isabel | 2,015 | nan | 1299--1304 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | b92513dac9d5b6a4683bcc625b94dd1ced98734e | 1 |
Gradiant-Analytics: Training Polarity Shifters with {CRF}s for Message Level Polarity Detection | Cerezo-Costas, H{\'e}ctor and
Celix-Salgado, Diego | 2,015 | nan | 539--544 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | a109aa9ab559dbdbd57df26ba2a5a08fba7aa34b | 0 |
Knowledge Base Population: Successful Approaches and Challenges | Ji, Heng and
Grishman, Ralph | 2,011 | nan | 1148--1158 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 77d2698e8efadda698b0edb457cd8de75224bfa0 | 1 |
Using machine translation in computer-aided translation to suggest the target-side words to change | Espl{\`a}-Gomis, Miquel and
S{\'a}nchez-Mart{\'\i}nez, Felipe and
Forcada, Mikel L. | 2,011 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | ffe80022365aa36087f736a57e536dffea78ab53 | 0 |
Fine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings | Abhishek, Abhishek and
Anand, Ashish and
Awekar, Amit | 2,017 | Fine-grained entity type classification (FETC) is the task of classifying an entity mention to a broad set of types. Distant supervision paradigm is extensively used to generate training data for this task. However, generated training data assigns same set of labels to every mention of an entity without considering its local context. Existing FETC systems have two major drawbacks: assuming training data to be noise free and use of hand crafted features. Our work overcomes both drawbacks. We propose a neural network model that jointly learns entity mentions and their context representation to eliminate use of hand crafted features. Our model treats training data as noisy and uses non-parametric variant of hinge loss function. Experiments show that the proposed model outperforms previous state-of-the-art methods on two publicly available datasets, namely FIGER (GOLD) and BBN with an average relative improvement of 2.69{\%} in micro-F1 score. Knowledge learnt by our model on one dataset can be transferred to other datasets while using same model or other FETC systems. These approaches of transferring knowledge further improve the performance of respective models. | 797--807 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | f4283dbf7883b1ab1a7fe01b58ebd627bcfdf008 | 1 |
Grounding Language by Continuous Observation of Instruction Following | Han, Ting and
Schlangen, David | 2,017 | Grounded semantics is typically learnt from utterance-level meaning representations (e.g., successful database retrievals, denoted objects in images, moves in a game). We explore learning word and utterance meanings by continuous observation of the actions of an instruction follower (IF). While an instruction giver (IG) provided a verbal description of a configuration of objects, IF recreated it using a GUI. Aligning these GUI actions to sub-utterance chunks allows a simple maximum entropy model to associate them as chunk meaning better than just providing it with the utterance-final configuration. This shows that semantics useful for incremental (word-by-word) application, as required in natural dialogue, might also be better acquired from incremental settings. | 491--496 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 0ceab2555088ce7ca49336f472f5902191661ff1 | 0 |
{M}essage {U}nderstanding {C}onference- 6: A Brief History | Grishman, Ralph and
Sundheim, Beth | 1,996 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 6723dda58e5e09089ec78ba42827b65859f030e2 | 1 |
{C}hinese String Searching Using the {KMP} Algorithm | Luk, Robert W.P. | 1,996 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 5f0d19f9fbbb03b8fb0fad8e810734da0242a60c | 0 |
Collective Cross-Document Relation Extraction Without Labelled Data | Yao, Limin and
Riedel, Sebastian and
McCallum, Andrew | 2,010 | nan | 1013--1023 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | a3fa819575c78be3cbcc8aa394fd21a182dce669 | 1 |
Employing Machine Translation in Glocalization Tasks {--} A Use Case Study | Sch{\"u}tz, J{\"o}rg and
Andr{\"a}, Sven Christian | 2,010 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 7dafa546ad602a551fda556e1db89c522e506a9d | 0 |
Class-Based \textit{n}-gram Models of Natural Language | Brown, Peter F. and
Della Pietra, Vincent J. and
deSouza, Peter V. and
Lai, Jenifer C. and
Mercer, Robert L. | 1,992 | nan | 467--480 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 3de5d40b60742e3dfa86b19e7f660962298492af | 1 |
{N}ew {Y}ork {U}niversity Description of the {PROTEUS} System as Used for {MUC}-4 | Grishman, Ralph and
Macleod, Catherine and
Sterling, John | 1,992 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 74384de9df7422e25c3e66749ec4674b474e13f8 | 0 |
Creating an Extended Named Entity Dictionary from {W}ikipedia | Higashinaka, Ryuichiro and
Sadamitsu, Kugatsu and
Saito, Kuniko and
Makino, Toshiro and
Matsuo, Yoshihiro | 2,012 | nan | 1163--1178 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 06d44cb242b2454527e6e5e0b020664ab65a3059 | 1 |
String Re-writing Kernel | Bu, Fan and
Li, Hang and
Zhu, Xiaoyan | 2,012 | nan | 449--458 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 32924bbd545430a09969fc700965f9e030c45e67 | 0 |
Constructing Datasets for Multi-hop Reading Comprehension Across Documents | Welbl, Johannes and
Stenetorp, Pontus and
Riedel, Sebastian | 2,018 | Most Reading Comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document. Enabling models to combine disjoint pieces of textual evidence would extend the scope of machine comprehension methods, but currently no resources exist to train and test this capability. We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods. In our task, a model learns to seek and combine evidence {---} effectively performing multihop, alias multi-step, inference. We devise a methodology to produce datasets for this task, given a collection of query-answer pairs and thematically linked documents. Two datasets from different domains are induced, and we identify potential pitfalls and devise circumvention strategies. We evaluate two previously proposed competitive models and find that one can integrate information across documents. However, both models struggle to select relevant information; and providing documents guaranteed to be relevant greatly improves their performance. While the models outperform several strong baselines, their best accuracy reaches 54.5{\%} on an annotated test set, compared to human performance at 85.0{\%}, leaving ample room for improvement. | 287--302 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 7d5cf22c70484fe217936c66741fb73b2a278bde | 1 |
{E}i{TAKA} at {S}em{E}val-2018 Task 1: An Ensemble of N-Channels {C}onv{N}et and {XG}boost Regressors for Emotion Analysis of Tweets | Jabreel, Mohammed and
Moreno, Antonio | 2,018 | This paper describes our system that has been used in Task1 Affect in Tweets. We combine two different approaches. The first one called N-Stream ConvNets, which is a deep learning approach where the second one is XGboost regressor based on a set of embedding and lexicons based features. Our system was evaluated on the testing sets of the tasks outperforming all other approaches for the Arabic version of valence intensity regression task and valence ordinal classification task. | 193--199 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 58ee3e694bcaa126d9f2438dc0326824a0de5584 | 0 |
Improving Fine-grained Entity Typing with Entity Linking | Dai, Hongliang and
Du, Donghong and
Li, Xin and
Song, Yangqiu | 2,019 | Fine-grained entity typing is a challenging problem since it usually involves a relatively large tag set and may require to understand the context of the entity mention. In this paper, we use entity linking to help with the fine-grained entity type classification process. We propose a deep neural model that makes predictions based on both the context and the information obtained from entity linking results. Experimental results on two commonly used datasets demonstrates the effectiveness of our approach. On both datasets, it achieves more than 5{\%} absolute strict accuracy improvement over the state of the art. | 6210--6215 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | b74b272c7fe881614f3eb8c2504b037439571eec | 1 |
Comparison of Diverse Decoding Methods from Conditional Language Models | Ippolito, Daphne and
Kriz, Reno and
Sedoc, Jo{\~a}o and
Kustikova, Maria and
Callison-Burch, Chris | 2,019 | While conditional language models have greatly improved in their ability to output high quality natural language, many NLP applications benefit from being able to generate a diverse set of candidate sequences. Diverse decoding strategies aim to, within a given-sized candidate list, cover as much of the space of high-quality outputs as possible, leading to improvements for tasks that rerank and combine candidate outputs. Standard decoding methods, such as beam search, optimize for generating high likelihood sequences rather than diverse ones, though recent work has focused on increasing diversity in these methods. In this work, we perform an extensive survey of decoding-time strategies for generating diverse outputs from a conditional language model. In addition, we present a novel method where we over-sample candidates, then use clustering to remove similar sequences, thus achieving high diversity without sacrificing quality. | 3752--3762 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | fd846869e6f25d9b1a524aef8b54a08b81a1b1fa | 0 |
Generating Fine-Grained Open Vocabulary Entity Type Descriptions | Bhowmik, Rajarshi and
de Melo, Gerard | 2,018 | While large-scale knowledge graphs provide vast amounts of structured facts about entities, a short textual description can often be useful to succinctly characterize an entity and its type. Unfortunately, many knowledge graphs entities lack such textual descriptions. In this paper, we introduce a dynamic memory-based network that generates a short open vocabulary description of an entity by jointly leveraging induced fact embeddings as well as the dynamic context of the generated sequence of words. We demonstrate the ability of our architecture to discern relevant information for more accurate generation of type description by pitting the system against several strong baselines. | 877--888 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | e2468753d57300d06accc5a31479e6803c90e5d4 | 1 |
Polyglot Semantic Parsing in {API}s | Richardson, Kyle and
Berant, Jonathan and
Kuhn, Jonas | 2,018 | Traditional approaches to semantic parsing (SP) work by training individual models for each available parallel dataset of text-meaning pairs. In this paper, we explore the idea of polyglot semantic translation, or learning semantic parsing models that are trained on multiple datasets and natural languages. In particular, we focus on translating text to code signature representations using the software component datasets of Richardson and Kuhn (2017b,a). The advantage of such models is that they can be used for parsing a wide variety of input natural languages and output programming languages, or mixed input languages, using a single unified model. To facilitate modeling of this type, we develop a novel graph-based decoding framework that achieves state-of-the-art performance on the above datasets, and apply this method to two other benchmark SP tasks. | 720--730 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | cf8ec112520e53a3864f01a827b085c3869c55e8 | 0 |
Fine Grained Classification of Named Entities | Fleischman, Michael and
Hovy, Eduard | 2,002 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 198b711915429fa55162e749a0b964755b36a62e | 1 |
How to prevent adjoining in {TAG}s and its impact on the Average Case Complexity | Woch, Jens | 2,002 | nan | 102--107 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | e0fb80262a15e457af9e0f3de46c1f35c8f8da82 | 0 |
Neural Architectures for Fine-grained Entity Type Classification | Shimaoka, Sonse and
Stenetorp, Pontus and
Inui, Kentaro and
Riedel, Sebastian | 2,017 | In this work, we investigate several neural network architectures for fine-grained entity type classification and make three key contributions. Despite being a natural comparison and addition, previous work on attentive neural architectures have not considered hand-crafted features and we combine these with learnt features and establish that they complement each other. Additionally, through quantitative analysis we establish that the attention mechanism learns to attend over syntactic heads and the phrase containing the mention, both of which are known to be strong hand-crafted features for our task. We introduce parameter sharing between labels through a hierarchical encoding method, that in low-dimensional projections show clear clusters for each type hierarchy. Lastly, despite using the same evaluation dataset, the literature frequently compare models trained using different data. We demonstrate that the choice of training data has a drastic impact on performance, which decreases by as much as 9.85{\%} loose micro F1 score for a previously proposed method. Despite this discrepancy, our best model achieves state-of-the-art results with 75.36{\%} loose micro F1 score on the well-established Figer (GOLD) dataset and we report the best results for models trained using publicly available data for the OntoNotes dataset with 64.93{\%} loose micro F1 score. | 1271--1280 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 800dd1672789fe97513b84e65e75e370b10d6c13 | 1 |
Question Difficulty {--} How to Estimate Without Norming, How to Use for Automated Grading | Pad{\'o}, Ulrike | 2,017 | Question difficulty estimates guide test creation, but are too costly for small-scale testing. We empirically verify that Bloom{'}s Taxonomy, a standard tool for difficulty estimation during question creation, reliably predicts question difficulty observed after testing in a short-answer corpus. We also find that difficulty is mirrored in the amount of variation in student answers, which can be computed before grading. We show that question difficulty and its approximations are useful for \textit{automated grading}, allowing us to identify the optimal feature set for grading each question even in an unseen-question setting. | 1--10 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 12f66979e1b1a7d8c2e1a4ff8a6219c19483138e | 0 |
Learning to Bootstrap for Entity Set Expansion | Yan, Lingyong and
Han, Xianpei and
Sun, Le and
He, Ben | 2,019 | Bootstrapping for Entity Set Expansion (ESE) aims at iteratively acquiring new instances of a specific target category. Traditional bootstrapping methods often suffer from two problems: 1) delayed feedback, i.e., the pattern evaluation relies on both its direct extraction quality and extraction quality in later iterations. 2) sparse supervision, i.e., only few seed entities are used as the supervision. To address the above two problems, we propose a novel bootstrapping method combining the Monte Carlo Tree Search (MCTS) algorithm with a deep similarity network, which can efficiently estimate delayed feedback for pattern evaluation and adaptively score entities given sparse supervision signals. Experimental results confirm the effectiveness of the proposed method. | 292--301 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | b2057b7ae7205c3fce709d349d575683a3dc40d1 | 1 |
Evidence Sentence Extraction for Machine Reading Comprehension | Wang, Hai and
Yu, Dian and
Sun, Kai and
Chen, Jianshu and
Yu, Dong and
McAllester, David and
Roth, Dan | 2,019 | Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks. However, it is still difficult to interpret the predictions of existing MRC models. In this paper, we focus on extracting evidence sentences that can explain or support the answers of multiple-choice MRC tasks, where the majority of answer options cannot be directly extracted from reference documents. Due to the lack of ground truth evidence sentence labels in most cases, we apply distant supervision to generate imperfect labels and then use them to train an evidence sentence extractor. To denoise the noisy labels, we apply a recently proposed deep probabilistic logic learning framework to incorporate both sentence-level and cross-sentence linguistic indicators for indirect supervision. We feed the extracted evidence sentences into existing MRC models and evaluate the end-to-end performance on three challenging multiple-choice MRC datasets: MultiRC, RACE, and DREAM, achieving comparable or better performance than the same models that take as input the full reference document. To the best of our knowledge, this is the first work extracting evidence sentences for multiple-choice MRC. | 696--707 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | bb104dc51121a0f64a5327526fad449cb03dd1bb | 0 |
Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss | Xu, Peng and
Barbosa, Denilson | 2,018 | The task of Fine-grained Entity Type Classification (FETC) consists of assigning types from a hierarchy to entity mentions in text. Existing methods rely on distant supervision and are thus susceptible to noisy labels that can be out-of-context or overly-specific for the training sentence. Previous methods that attempt to address these issues do so with heuristics or with the help of hand-crafted features. Instead, we propose an end-to-end solution with a neural network model that uses a variant of cross-entropy loss function to handle out-of-context labels, and hierarchical loss normalization to cope with overly-specific ones. Also, previous work solve FETC a multi-label classification followed by ad-hoc post-processing. In contrast, our solution is more elegant: we use public word embeddings to train a single-label that jointly learns representations for entity mentions and their context. We show experimentally that our approach is robust against noise and consistently outperforms the state-of-the-art on established benchmarks for the task. | 16--25 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 008405f7ee96677ac23cc38be360832af2d9f437 | 1 |
Cross-Domain Sentiment Classification with Target Domain Specific Information | Peng, Minlong and
Zhang, Qi and
Jiang, Yu-gang and
Huang, Xuanjing | 2,018 | The task of adopting a model with good performance to a target domain that is different from the source domain used for training has received considerable attention in sentiment analysis. Most existing approaches mainly focus on learning representations that are domain-invariant in both the source and target domains. Few of them pay attention to domain-specific information, which should also be informative. In this work, we propose a method to simultaneously extract domain specific and invariant representations and train a classifier on each of the representation, respectively. And we introduce a few target domain labeled data for learning domain-specific information. To effectively utilize the target domain labeled data, we train the domain invariant representation based classifier with both the source and target domain labeled data and train the domain-specific representation based classifier with only the target domain labeled data. These two classifiers then boost each other in a co-training style. Extensive sentiment analysis experiments demonstrated that the proposed method could achieve better performance than state-of-the-art methods. | 2505--2513 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | a99122b0b96cc40982ac267ba7e99c72a1dcc2e9 | 0 |
An Attentive Neural Architecture for Fine-grained Entity Type Classification | Shimaoka, Sonse and
Stenetorp, Pontus and
Inui, Kentaro and
Riedel, Sebastian | 2,016 | nan | 69--74 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 5c22ff7fe5fc588e3648b5897255f151feb61fee | 1 |
Producing Monolingual and Parallel Web Corpora at the Same Time - {S}pider{L}ing and Bitextor{'}s Love Affair | Ljube{\v{s}}i{\'c}, Nikola and
Espl{\`a}-Gomis, Miquel and
Toral, Antonio and
Rojas, Sergio Ortiz and
Klubi{\v{c}}ka, Filip | 2,016 | This paper presents an approach for building large monolingual corpora and, at the same time, extracting parallel data by crawling the top-level domain of a given language of interest. For gathering linguistically relevant data from top-level domains we use the SpiderLing crawler, modified to crawl data written in multiple languages. The output of this process is then fed to Bitextor, a tool for harvesting parallel data from a collection of documents. We call the system combining these two tools Spidextor, a blend of the names of its two crucial parts. We evaluate the described approach intrinsically by measuring the accuracy of the extracted bitexts from the Croatian top-level domain {``}.hr{''} and the Slovene top-level domain {``}.si{''}, and extrinsically on the English-Croatian language pair by comparing an SMT system built from the crawled data with third-party systems. We finally present parallel datasets collected with our approach for the English-Croatian, English-Finnish, English-Serbian and English-Slovene language pairs. | 2949--2956 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | e5ce9182054fa811d3c65c7e98b30bf2d90af8a4 | 0 |
Assessing the Challenge of Fine-Grained Named Entity Recognition and Classification | Ekbal, Asif and
Sourjikova, Eva and
Frank, Anette and
Ponzetto, Simone Paolo | 2,010 | nan | 93--101 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 9538eed00e2ed9d077c88afe7492766f6855ba78 | 1 |
Workshop on Advanced Corpus Solutions | Johannessen, Janne Bondi | 2,010 | nan | 717--719 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | a33ace522fd2a4dd26cb02a4d62eec8436e8f6b3 | 0 |
An Attentive Fine-Grained Entity Typing Model with Latent Type Representation | Lin, Ying and
Ji, Heng | 2,019 | We propose a fine-grained entity typing model with a novel attention mechanism and a hybrid type classifier. We advance existing methods in two aspects: feature extraction and type prediction. To capture richer contextual information, we adopt contextualized word representations instead of fixed word embeddings used in previous work. In addition, we propose a two-step mention-aware attention mechanism to enable the model to focus on important words in mentions and contexts. We also present a hybrid classification method beyond binary relevance to exploit type inter-dependency with latent type representation. Instead of independently predicting each type, we predict a low-dimensional vector that encodes latent type features and reconstruct the type vector from this latent representation. Experiment results on multiple data sets show that our model significantly advances the state-of-the-art on fine-grained entity typing, obtaining up to 6.1{\%} and 5.5{\%} absolute gains in macro averaged F-score and micro averaged F-score respectively. | 6197--6202 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | ed3a6ff80bd9892a5d8bf6490147fcd518ebc413 | 1 |
An adaptable task-oriented dialog system for stand-alone embedded devices | Duong, Long and
Hoang, Vu Cong Duy and
Pham, Tuyen Quang and
Hong, Yu-Heng and
Dovgalecs, Vladislavs and
Bashkansky, Guy and
Black, Jason and
Bleeker, Andrew and
Huitouze, Serge Le and
Johnson, Mark | 2,019 | This paper describes a spoken-language end-to-end task-oriented dialogue system for small embedded devices such as home appliances. While the current system implements a smart alarm clock with advanced calendar scheduling functionality, the system is designed to make it easy to port to other application domains (e.g., the dialogue component factors out domain-specific execution from domain-general actions such as requesting and updating slot values). The system does not require internet connectivity because all components, including speech recognition, natural language understanding, dialogue management, execution and text-to-speech, run locally on the embedded device (our demo uses a Raspberry Pi). This simplifies deployment, minimizes server costs and most importantly, eliminates user privacy risks. The demo video in alarm domain is here youtu.be/N3IBMGocvHU | 49--57 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | ea2ad7e330070aed3e909a2e263ae88e320663b3 | 0 |
Transforming {W}ikipedia into a Large-Scale Fine-Grained Entity Type Corpus | Ghaddar, Abbas and
Langlais, Philippe | 2,018 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 8296e7f869172ac6fe34380574706f753328eda5 | 1 |
{B}in{L}in: A Simple Method of Dependency Tree Linearization | Puzikov, Yevgeniy and
Gurevych, Iryna | 2,018 | Surface Realization Shared Task 2018 is a workshop on generating sentences from lemmatized sets of dependency triples. This paper describes the results of our participation in the challenge. We develop a data-driven pipeline system which first orders the lemmas and then conjugates the words to finish the surface realization process. Our contribution is a novel sequential method of ordering lemmas, which, despite its simplicity, achieves promising results. We demonstrate the effectiveness of the proposed approach, describe its limitations and outline ways to improve it. | 13--28 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 933afbd7aea1671e6950d65511c23af7adf38de1 | 0 |
A Joint Model for Entity Analysis: Coreference, Typing, and Linking | Durrett, Greg and
Klein, Dan | 2,014 | We present a joint model of three core tasks in the entity analysis stack: coreference resolution (within-document clustering), named entity recognition (coarse semantic typing), and entity linking (matching to Wikipedia entities). Our model is formally a structured conditional random field. Unary factors encode local features from strong baselines for each task. We then add binary and ternary factors to capture cross-task interactions, such as the constraint that coreferent mentions have the same semantic type. On the ACE 2005 and OntoNotes datasets, we achieve state-of-the-art results for all three tasks. Moreover, joint modeling improves performance on each task over strong independent baselines. | 477--490 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 28eb033eee5f51c5e5389cbb6b777779203a6778 | 1 |
Transfer learning of feedback head expressions in {D}anish and {P}olish comparable multimodal corpora | Navarretta, Costanza and
Lis, Magdalena | 2,014 | The paper is an investigation of the reusability of the annotations of head movements in a corpus in a language to predict the feedback functions of head movements in a comparable corpus in another language. The two corpora consist of naturally occurring triadic conversations in Danish and Polish, which were annotated according to the same scheme. The intersection of common annotation features was used in the experiments. A Na{\"\i}ve Bayes classifier was trained on the annotations of a corpus and tested on the annotations of the other corpus. Training and test datasets were then reversed and the experiments repeated. The results show that the classifier identifies more feedback behaviours than the majority baseline in both cases and the improvements are significant. The performance of the classifier decreases significantly compared with the results obtained when training and test data belong to the same corpus. Annotating multimodal data is resource consuming, thus the results are promising. However, they also confirm preceding studies that have identified both similarities and differences in the use of feedback head movements in different languages. Since our datasets are small and only regard a communicative behaviour in two languages, the experiments should be tested on more data types. | 3597--3603 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | afd1491f8a07ce7a4388e0ab40bf4eb7c06333a8 | 0 |
{W}iki{C}oref: An {E}nglish Coreference-annotated Corpus of {W}ikipedia Articles | Ghaddar, Abbas and
Langlais, Phillippe | 2,016 | This paper presents WikiCoref, an English corpus annotated for anaphoric relations, where all documents are from the English version of Wikipedia. Our annotation scheme follows the one of OntoNotes with a few disparities. We annotated each markable with coreference type, mention type and the equivalent Freebase topic. Since most similar annotation efforts concentrate on very specific types of written text, mainly newswire, there is a lack of resources for otherwise over-used Wikipedia texts. The corpus described in this paper addresses this issue. We present a freely available resource we initially devised for improving coreference resolution algorithms dedicated to Wikipedia texts. Our corpus has no restriction on the topics of the documents being annotated, and documents of various sizes have been considered for annotation. | 136--142 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | fe5cffa25cb2ab412da9f19ea7e9656ac72d454c | 1 |
Improving Reliability of Word Similarity Evaluation by Redesigning Annotation Task and Performance Measure | Avraham, Oded and
Goldberg, Yoav | 2,016 | nan | 106--110 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 1c7174bd2b01831920217088e2b48cb151691110 | 0 |