Finding predominant word senses in untagged text

McCarthy, Diana, Koeling, Rob, Weeds, Julie and Carroll, John (2004) Finding predominant word senses in untagged text. In: 42nd Annual Meeting of the Association for Computational Linguistics, Barcelona, Spain.

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Abstract

In word sense disambiguation (WSD), the heuristic of choosing the most common sense is extremely powerful because the distribution of the senses of a word is often skewed. The problem with using the predominant, or first sense heuristic, aside from the fact that it does not take surrounding context into account, is that it assumes some quantity of hand-tagged data. Whilst there are a few hand-tagged corpora available for some languages, one would expect the frequency distribution of the senses of words, particularly topical words, to depend on the genre and domain of the text under consideration. We present work on the use of a thesaurus acquired from raw textual corpora and the WordNet similarity package to find predominant noun senses automatically. The acquired predominant senses give a precision of 64% on the nouns of the SENSEVAL-2 English all-words task. This is a very promising result given that our method does not require any hand-tagged text, such as SemCor. Furthermore, we demonstrate that our method discovers appropriate predominant senses for words from two domain-specific corpora.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Originality: Description of a novel, unsupervised method for acquiring information about predominant senses of words - for use as priors in word sense disambiguation - with accuracy approaching that of a supervised technique. Rigour: Method evaluated on the standard word sense disambiguation data; also indicative results for domain-specific text from the Reuters corpus. Significance: Likely to become the backoff method of choice for sense disambiguation of text in specific domains, and for languages other than English. Outlet/citations: Best Paper Award at the most prestigious annual international conference on natural language processing. First such award to a paper on unsupervised learning. Google Scholar 41 citations.
Schools and Departments: School of Engineering and Informatics > Informatics
Depositing User: Diana Frances McCarthy
Date Deposited: 06 Feb 2012 20:36
Last Modified: 12 Apr 2012 11:36
URI: http://sro.sussex.ac.uk/id/eprint/26921
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