Finding Predominant Word Senses in Untagged Text

Finding Predominant Word Senses in Untagged Text Diana McCarthy & Rob Koeling & Julie Weeds & John Carroll Department of Informatics, University of Sussex Brighton BN1 9QH, UK � dianam,robk,juliewe,johnca � @sussex.ac.uk 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 similar- ity package to find predominant noun senses auto- matically. 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 appropri- ate predominant senses for words from two domain- specific corpora. 1 Introduction The first sense heuristic which is often used as a baseline for supervised WSD systems outperforms many of these systems which take surrounding con- text into account. This is shown by the results of the English all-words task in SENSEVAL-2 (Cot- ton et al., 1998) in figure 1 below, where the first sense is that listed in WordNet for the PoS given by the Penn TreeBank (Palmer et al., 2001). The senses in WordNet are ordered according to the fre- quency data in the manually tagged resource Sem- Cor (Miller et al., 1993). Senses that have not oc- curred in SemCor are ordered arbitrarily and af- ter those senses of the word that have occurred. The figure distinguishes systems which make use of hand-tagged data (using HTD) such as SemCor, from those that do not (without HTD). The high per- formance of the first sense baseline is due to the skewed frequency distribution of word senses. Even systems which show superior performance to this heuristic often make use of the heuristic where ev- idence from the context is not sufficient (Hoste et al., 2001). Whilst a first sense heuristic based on a sense-tagged corpus such as SemCor is clearly use- ful, there is a strong case for obtaining a first, or pre- dominant, sense from untagged corpus data so that a WSD system can be tuned to the genre or domain at hand. SemCor comprises a relatively small sample of 250,000 words. There are words where the first sense in WordNet is counter-intuitive, because of the size of the corpus, and because where the fre- quency data does not indicate a first sense, the or- dering is arbitrary. For example the first sense of …

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