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Word Sense Disambiguation

2013
This chapter discusses the basic concepts of Word Sense Disambiguation (WSD) and the approaches to solving this problem. Both general purpose WSD and domain specific WSD are presented. The first part of the discussion focuses on existing approaches for WSD, including knowledge-based, supervised, semi-supervised, unsupervised, hybrid, and bilingual ...
Pushpak Bhattacharyya, Mitesh Khapra
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Word Sense Induction and Disambiguation [PDF]

open access: possible, 2011
Major difficulties in language processing are caused by the fact that many words are ambiguous, i.e. they have different meanings in different contexts, but are written (or pronounced) in the same way. While syntactic ambiguities have already been addressed in the previous chapter, now the focus is set on the semantic dimension of this problem. In this
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Word sense disambiguation methods

Programming and Computer Software, 2010
Word sense disambiguation is one of the key tasks of text processing. It consists in the determination of senses of words or compound terms in accordance with the context where they were used. The word sense disambiguation problem originated in the 1950s as a subtask of machine translation.
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Word-Sense Disambiguation

2012
Word-sense disambiguation (WSD) is the process of identifying the meanings of words in context. This article begins with discussing the origins of the problem in the earliest machine translation systems. Early attempts to solve the WSD problem suffered from a lack of coverage.
Mark Stevenson, Yorick Wilks
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Word Sense Disambiguation

2018
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.
Eneko Agirre, Mark Stevenson
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Word-Sense Disambiguation by Examples

1993
This chapter describes a method of disambiguating multi-sense words in a sentence by using example sentences in which such words are already disambiguated, and by using taxonym and synonym hierarchies. As a knowledge base, we developed a small-scale text database containing 730 example sentences in English that include the verb “take,” and prototyped a
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Adaptive Graph Guided Disambiguation for Partial Label Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
Deng-bao Wang, Min-ling Zhang, Li Li
exaly  

Self‐training author name disambiguation for information scarce scenarios

Journal of the Association for Information Science and Technology, 2014
Adriano Veloso   +2 more
exaly  

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