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Title: Anaphora and coreference resolution : a review
Authors: Sukthanker, Rhea
Poria, Soujanya
Cambria, Erik
Thirunavukarasu, Ramkumar
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Sukthanker, R., Poria, S., Cambria, E. & Thirunavukarasu, R. (2020). Anaphora and coreference resolution : a review. Information Fusion, 59, 139-162.
Project: A19E2b0098
Journal: Information Fusion
Abstract: 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.
ISSN: 1566-2535
DOI: 10.1016/j.inffus.2020.01.010
Schools: School of Computer Science and Engineering 
Rights: © 2020 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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