
Contents
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27.1 Introduction 27.1 Introduction
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27.2 Key Concepts 27.2 Key Concepts
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27.3 Knowledge-Based Approaches 27.3 Knowledge-Based Approaches
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27.3.1 Early Approaches 27.3.1 Early Approaches
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27.3.2 Dictionary Definition Overlap 27.3.2 Dictionary Definition Overlap
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27.3.3 Using Multiple Knowledge Sources from MRDs 27.3.3 Using Multiple Knowledge Sources from MRDs
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27.3.4 Graph-Based Approaches 27.3.4 Graph-Based Approaches
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27.3.5 Lexical Similarity 27.3.5 Lexical Similarity
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27.4 Supervised Machine Learning Approaches 27.4 Supervised Machine Learning Approaches
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27.4.1 Representing Context 27.4.1 Representing Context
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27.4.2 Disambiguation 27.4.2 Disambiguation
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27.4.3 Two Claims about Senses 27.4.3 Two Claims about Senses
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27.5 Unsupervised and Minimally Supervised Approaches 27.5 Unsupervised and Minimally Supervised Approaches
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27.6 Domain-Specific WSD 27.6 Domain-Specific WSD
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27.6.1 Domain Adaptation 27.6.1 Domain Adaptation
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27.6.2 WSD in Particular Domains 27.6.2 WSD in Particular Domains
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27.7 Evaluation 27.7 Evaluation
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27.8 Applying WSD 27.8 Applying WSD
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27.9 Conclusion 27.9 Conclusion
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Further Reading and Relevant Resources Further Reading and Relevant Resources
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References References
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27 Word Sense Disambiguation
Get accessEneko Agirre obtained his PhD in computer science from the University of the Basque Country, UPV/EHU, and his MSc from Edinburgh University. He is currently Professor in the Computer Science Faculty in UPV/EHU. He has published over 150 international papers in NLP. He has been president of the ACL SIGLEX and member of the editorial board of Computational Linguistics. He is co-founder of the Joint Conference on Lexical and Computational Semantics (*SEM). He received a Google Research Award in 2016.
Mark Stevenson is a Senior Lecturer within the Natural Language Processing group of Sheffield University’s Computer Science Department. He has worked on a range of topics including lexical semantics, word sense disambiguation, semantic similarity, information extraction, and text reuse. His publications include a monograph and over 120 papers in journals, collected volumes, and international conferences. He has twice been elected onto the SIGLEX board and has been a member of the editorial board of Computational Linguistics. He has held fellowships at Stanford University and British Telecom, was an EPSRC Advanced Research Fellow (2006–2011), and has coordinated an EU project.
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Published:05 February 2018
Cite
Abstract
Word sense disambiguation (WSD) is the process of identifying the meanings of words in context. The difficulty of this problem stems from the subtlety of word sense differences and the need for some level of understanding. This chapter describes the main approaches to the problem, methods for evaluating performance, and potential applications. Recent developments in deep learning have led to renewed interest in this long-established task.
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