Word Sense Disambiguation in Untagged Text based on Term Weight Learning

Proceedings of EACL '99 Word Sense Disambiguation in Untagged Text based on Term Weight Learning Fumiyo Fukumoto and Yoshimi Suzukit Department of Computer Science and Media Engineering, Yamanashi University 4-3-11 Takeda, Kofu 400-8511 Japan {fukumoto@skye.esb, ysuzuki@windermere.alpsl.esit }.yamanashi.ac.jp Abstract This paper describes unsupervised learn- ing algorithm for disambiguating verbal word senses using term weight learning. In our method, collocations which char- acterise every sense are extracted using similarity-based estimation. For the re- sults, term weight learning is performed. Parameters of term weighting are then estimated so as to maximise the colloca- tions which characterise every sense and minimise the other collocations. The re- suits of experiment demonstrate the ef- fectiveness of the method. 1 Introduction One of the major approaches to disambiguate word senses is supervised learning (Gale et al., 1992), (Yarowsky, 1992), (Bruce and Janyce, 1994), (Miller et al., 1994), (Niwa and Nitta, 1994), (Luk, 1995), (Ng and Lee, 1996), (Wilks and Stevenson, 1998). However, a major obstacle impedes the acquisition of lexical knowledge from corpora, i.e. the difficulties of manually sense- tagging a training corpus, since this limits the ap- plicability of many approaches to domains where this hard to acquire knowledge is already avail- able. This paper describes unsupervised learning al- gorithm for disambiguating verbal word senses us- ing term weight learning. In our approach, an overlapping clustering algorithm based on Mutual information-based (Mu) term weight learning be- tween a verb and a noun is applied to a set of verbs. It is preferable that Mu is not low (Mu(x,y) _> 3) for a reliable statistical analysis (Church et al., 1991). However, this suffers from the problem of data sparseness, i.e. the co-occurrences which are used to represent every distinct senses does not appear in the test data. To attack this prob- lem, for a low Mu value, we distinguish between unobserved co-occurrences that are likely to oc- cur in a new corpus and those that are not, by using similarity-based estimation between two co- occurrences of words. For the results, term weight learning is performed. Parameters of term weight- ing are then estimated so as to maximise the col- locations which characterise every sense and min- imise the other collocations. In the following sections, we first define a pol- ysemy from the viewpoint of clustering, then de- scribe how to extract collocations using similarity- based estimation. Next, we present a clustering method and a method for verbal word sense dis- ambiguation using the result of clustering. Fi- nally, we report on an experiment in order to show the effect of the method. 2 Polysemy in Context Most previous corpus-based WSD algorithms are based on the fact that semantically similar words appear in a similar context. Semantically sim- ilar verbs, for example, co-occur with the same nouns. The following sentences from the Wall Street Journal show polysemous usages of take. (sl) Coke has typically taken a minority stake in such ventures. (sl') Guber and pepers tried to buy a stake in mgm in 1988. (s2) That process of …

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