
Contents
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30.1 Basic Notions and Terminology 30.1 Basic Notions and Terminology
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30.2 Anaphora: Linguistic Fundamentals 30.2 Anaphora: Linguistic Fundamentals
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30.3 Anaphora Resolution 30.3 Anaphora Resolution
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30.3.1 Identification of Anaphors 30.3.1 Identification of Anaphors
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30.3.2 Location of the Candidates for Antecedents 30.3.2 Location of the Candidates for Antecedents
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30.3.3 The Resolution Algorithm: Factors in Anaphora Resolution 30.3.3 The Resolution Algorithm: Factors in Anaphora Resolution
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30.4 Algorithms for Anaphora Resolution 30.4 Algorithms for Anaphora Resolution
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30.4.1 Approaches Based on Full Parsing 30.4.1 Approaches Based on Full Parsing
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30.4.2 Approaches Based on Partial Parsing 30.4.2 Approaches Based on Partial Parsing
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30.5 Evaluation of Anaphora Resolution Approaches 30.5 Evaluation of Anaphora Resolution Approaches
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30.6 Recent Work on Deep Learning for Anaphora and Coreference Resolution 30.6 Recent Work on Deep Learning for Anaphora and Coreference Resolution
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30.7 Applications of Anaphora Resolution 30.7 Applications of Anaphora Resolution
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Further Reading and Relevant Resources Further Reading and Relevant Resources
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References References
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30 Anaphora Resolution
Get accessRuslan Mitkov’s research covers 250 publications on various topics of Natural Language Processing. He is author of the monograph Anaphora Resolution (Longman, 2002), Editor of The Oxford Handbook of Computational Linguistics (Oxford University Press), Executive Editor of the Journal of Natural Language Engineering (Cambridge University Press), Editor-in-Chief of the John Benjamins NLP book series, and has been programme chair and keynote speaker of many international conferences. He is Director of the Research Institute in Information and Language Processing at the University of Wolverhampton. In recognition of his outstanding research/professional achievements, he has twice been awarded the title of Doctor honoris causa.
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Published:10 November 2020
Cite
Abstract
This chapter provides a theoretical background of anaphora and introduces the varieties of this pervasive linguistic phenomenon. Next, it defines the task of anaphora resolution and introduces it as a three-stage process: identification of anaphors, location of the candidates for antecedents, and the resolution algorithm. After that, the chapter outlines a selection of influential and extensively cited anaphora resolution algorithms and proceeds to discuss issues related to the evaluation of anaphora resolution algorithms. Recent deep learning work on anaphora and coreference resolution is briefly presented as well. Finally, the chapter explains why anaphora resolution is important for various NLP applications.
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