Finding Predominant Word Senses in Untagged Text

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Finding Predominant Word Senses in Untagged Text (2004)

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by Diana Mccarthy , Rob Koeling , Julie Weeds , John Carroll

Venue:	In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics
Citations:	107 - 4 self

BibTeX

@INPROCEEDINGS{Mccarthy04findingpredominant,
    author = {Diana Mccarthy and Rob Koeling and Julie Weeds and John Carroll},
    title = {Finding Predominant Word Senses in Untagged Text},
    booktitle = {In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics},
    year = {2004},
    pages = {280--287}
}

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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 handtagged 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 domainspecific corpora.

Keyphrases

untagged text predominant word sens predominant sens wordnet similarity package method discovers domainspecific corpus frequency distribution word sense disambiguation hand-tagged corpus raw textual corpus common sense english all-words task hand-tagged text predominant noun sens present work topical word promising result first sense heuristic

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