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Unsupervised similarity-based word sense disambiguation using context vectors and sentential word importance

Unsupervised similarity-based word sense disambiguation using context vectors and sentential word... Unsupervised Similarity-Based Word Sense Disambiguation Using Context Vectors and Sentential Word Importance KHALED ABDALGADER and ANDREW SKABAR, La Trobe University The process of identifying the actual meanings of words in a given text fragment has a long history in the eld of computational linguistics. Due to its importance in understanding the semantics of natural language, it is considered one of the most challenging problems facing this eld. In this article we propose a new unsupervised similarity-based word sense disambiguation (WSD) algorithm that operates by computing the semantic similarity between glosses of the target word and a context vector. The sense of the target word is determined as that for which the similarity between gloss and context vector is greatest. Thus, whereas conventional unsupervised WSD methods are based on measuring pairwise similarity between words, our approach is based on measuring semantic similarity between sentences. This enables it to utilize a higher degree of semantic information, and is more consistent with the way that human beings disambiguate; that is, by considering the greater context in which the word appears. We also show how performance can be further improved by incorporating a preliminary step in which the relative importance of words http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Speech and Language Processing (TSLP) Association for Computing Machinery

Unsupervised similarity-based word sense disambiguation using context vectors and sentential word importance

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2012 by ACM Inc.
ISSN
1550-4875
DOI
10.1145/2168748.2168750
Publisher site
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Abstract

Unsupervised Similarity-Based Word Sense Disambiguation Using Context Vectors and Sentential Word Importance KHALED ABDALGADER and ANDREW SKABAR, La Trobe University The process of identifying the actual meanings of words in a given text fragment has a long history in the eld of computational linguistics. Due to its importance in understanding the semantics of natural language, it is considered one of the most challenging problems facing this eld. In this article we propose a new unsupervised similarity-based word sense disambiguation (WSD) algorithm that operates by computing the semantic similarity between glosses of the target word and a context vector. The sense of the target word is determined as that for which the similarity between gloss and context vector is greatest. Thus, whereas conventional unsupervised WSD methods are based on measuring pairwise similarity between words, our approach is based on measuring semantic similarity between sentences. This enables it to utilize a higher degree of semantic information, and is more consistent with the way that human beings disambiguate; that is, by considering the greater context in which the word appears. We also show how performance can be further improved by incorporating a preliminary step in which the relative importance of words

Journal

ACM Transactions on Speech and Language Processing (TSLP) – Association for Computing Machinery

Published: May 1, 2012

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