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Word Sense Disambiguation Based on Word Sense Clustering [PDF]
, 2006 In this paper we address the problem of Word Sense Disambiguation by introducing a knowledge-driven framework for the disambiguation of nouns. The proposal is based on the clustering of noun sense representations and it serves as a general model that includes some existing disambiguation methods.
Aurora Pons-Porrata +2 more
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Practice of Word Sense Disambiguation
2018The paper aims at the community of researchers and practitioners that work in the area of natural language processing but do not specialize in the word sense disambiguation (WSD). It contains a brief introduction into WSD and describes the classical approaches to solve the problem.
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Multiwords and Word Sense Disambiguation
2005This paper studies the impact of multiword expressions on Word Sense Disambiguation (WSD). Several identification strategies of the multiwords in WordNet2.0 are tested in a real Senseval-3 task: the disambiguation of WordNet glosses. Although we have focused on Word Sense Disambiguation, the same techniques could be applied in more complex tasks, such ...
Victoria Arranz +2 more
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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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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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Probabilistic word sense disambiguation
Computer Speech & Language, 2004We present a theoretically motivated method for creating probabilistic word sense disambiguation (WSD) systems. The method works by composing multiple probabilistic components: such modularity is made possible by an application of Bayesian statistics and Lidstone's smoothing method. We show that a probabilistic WSD system created along these lines is a
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Word sense disambiguation methods
Programming and Computer Software, 2010Word 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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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 (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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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 (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
1993This 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, 2022Deng-bao Wang, Min-ling Zhang, Li Li
exaly