Anaphora and coreference resolution : a review
Coreference resolution aims at resolving repeated references to an object in a document and forms a core component of natural language processing (NLP) research. When used as a component in the processing pipeline of other NLP fields like machine translation, sentiment analysis, paraphrase detection...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Online Access: | https://hdl.handle.net/10356/155276 |
| _version_ | 1826126237739778048 |
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| author | Sukthanker, Rhea Poria, Soujanya Cambria, Erik Thirunavukarasu, Ramkumar |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Sukthanker, Rhea Poria, Soujanya Cambria, Erik Thirunavukarasu, Ramkumar |
| author_sort | Sukthanker, Rhea |
| collection | NTU |
| description | Coreference resolution aims at resolving repeated references to an object in a document and forms a core component of natural language processing (NLP) research. When used as a component in the processing pipeline of other NLP fields like machine translation, sentiment analysis, paraphrase detection, and summarization, coreference resolution has a potential to highly improve accuracy. A direction of research closely related to coreference resolution is anaphora resolution. Existing literature is often ambiguous in its usage of these terms and often uses them interchangeably. Through this review article, we clarify the scope of these two tasks. We also carry out a detailed analysis of the datasets, evaluation metrics and research methods that have been adopted to tackle these NLP problems. This survey is motivated by the aim of providing readers with a clear understanding of what constitutes these two tasks in NLP research and their related issues. |
| first_indexed | 2024-10-01T06:49:23Z |
| format | Journal Article |
| id | ntu-10356/155276 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:49:23Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1552762022-03-17T02:26:33Z Anaphora and coreference resolution : a review Sukthanker, Rhea Poria, Soujanya Cambria, Erik Thirunavukarasu, Ramkumar School of Computer Science and Engineering Engineering::Computer science and engineering Coreference Resolution Anaphora Resolution Coreference resolution aims at resolving repeated references to an object in a document and forms a core component of natural language processing (NLP) research. When used as a component in the processing pipeline of other NLP fields like machine translation, sentiment analysis, paraphrase detection, and summarization, coreference resolution has a potential to highly improve accuracy. A direction of research closely related to coreference resolution is anaphora resolution. Existing literature is often ambiguous in its usage of these terms and often uses them interchangeably. Through this review article, we clarify the scope of these two tasks. We also carry out a detailed analysis of the datasets, evaluation metrics and research methods that have been adopted to tackle these NLP problems. This survey is motivated by the aim of providing readers with a clear understanding of what constitutes these two tasks in NLP research and their related issues. Agency for Science, Technology and Research (A*STAR) This research is supported by the Agency for Science, Technology and Research (A∗STAR) under its AME Programmatic Funding Scheme (Projects #A18A2b0046 and #A19E2b0098). 2022-03-17T02:26:33Z 2022-03-17T02:26:33Z 2020 Journal Article Sukthanker, R., Poria, S., Cambria, E. & Thirunavukarasu, R. (2020). Anaphora and coreference resolution : a review. Information Fusion, 59, 139-162. https://dx.doi.org/10.1016/j.inffus.2020.01.010 1566-2535 https://hdl.handle.net/10356/155276 10.1016/j.inffus.2020.01.010 2-s2.0-85079634946 59 139 162 en A19E2b0098 A18A2b0046 Information Fusion © 2020 Elsevier B.V. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Coreference Resolution Anaphora Resolution Sukthanker, Rhea Poria, Soujanya Cambria, Erik Thirunavukarasu, Ramkumar Anaphora and coreference resolution : a review |
| title | Anaphora and coreference resolution : a review |
| title_full | Anaphora and coreference resolution : a review |
| title_fullStr | Anaphora and coreference resolution : a review |
| title_full_unstemmed | Anaphora and coreference resolution : a review |
| title_short | Anaphora and coreference resolution : a review |
| title_sort | anaphora and coreference resolution a review |
| topic | Engineering::Computer science and engineering Coreference Resolution Anaphora Resolution |
| url | https://hdl.handle.net/10356/155276 |
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