title stringlengths 5 342 | author stringlengths 3 2.17k | year int64 1.95k 2.02k | abstract stringlengths 0 12.7k | pages stringlengths 1 702 | queryID stringlengths 4 40 | query stringlengths 1 300 | paperID stringlengths 0 40 | include int64 0 1 |
|---|---|---|---|---|---|---|---|---|
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 l... | 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 fr... | 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... | 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 inspire... | 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 ... | 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... | 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 ... | 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 shar... | 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 doc... | 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 recog... | 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 annotat... | 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 c... | 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 pape... | 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 BE... | 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 ... | 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 techni... | 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 ... | 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 bene... | 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, delexicalis... | 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 ... | 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... | 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... | 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-engin... | 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 m... | 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 cor... | 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 com... | 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 co... | 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 wor... | 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... | 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... | 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... | 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 ... | 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 predi... | 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-qu... | 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 ... | 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 particula... | 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 feat... | 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 diffi... | 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 iterati... | 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 MR... | 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... | 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. F... | 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 mult... | 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... | 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., ... | 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 realiz... | 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 ... | 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 ... | 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. ... | 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 |
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