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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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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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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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Smoothing and Word Sense Disambiguation
2004This paper presents an algorithm to apply the smoothing techniques described in [15] to three different Machine Learning (ML) methods for Word Sense Disambiguation (WSD). The method to obtain better estimations for the features is explained step by step, and applied to n-way ambiguities.
Eneko Agirre, David Martinez
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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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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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Word Sense Induction and Disambiguation
2011Major 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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Unsupervised word embeddings capture latent knowledge from materials science literature
Nature, 2019Vahe Tshitoyan +2 more
exaly