Anaphora and Coreference Resolution: A Review

May 30, 2018 ยท The Cartographer ยท ๐Ÿ› Information Fusion

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
Survey/review paper โ€” maps the landscape rather than implementing a method.

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"Title-pattern auto-detect: Anaphora and Coreference Resolution: A Review"

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Authors Rhea Sukthanker, Soujanya Poria, Erik Cambria, Ramkumar Thirunavukarasu arXiv ID 1805.11824 Category cs.CL: Computation & Language Citations 185 Venue Information Fusion Last Checked 1 day ago
Abstract
Entity resolution aims at resolving repeated references to an entity in a document and forms a core component of natural language processing (NLP) research. This field possesses immense potential to improve the performance of other NLP fields like machine translation, sentiment analysis, paraphrase detection, summarization, etc. The area of entity resolution in NLP has seen proliferation of research in two separate sub-areas namely: anaphora resolution and coreference resolution. Through this review article, we aim at clarifying the scope of these two tasks in entity resolution. We also carry out a detailed analysis of the datasets, evaluation metrics and research methods that have been adopted to tackle this NLP problem. This survey is motivated with the aim of providing the reader with a clear understanding of what constitutes this NLP problem and the issues that require attention.
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