This paper describes unsupervised learning algorithm for disambiguating verbal word senses using term weight learning. In our method, collocations which characterise every sense are extracted using similarity-based estimation. For the results, term weight learning is performed. Parameters of term weighting are then estimated so as to maximise the collocations which characterise every sense and minimise the other collocations. The resuits of experiment demonstrate the effectiveness of the method. 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 sensetagging a training corpus, since this limits the applicability of many approaches to domains where this hard to acquire knowledge is already available. This paper describes unsupervised learning algorithm for disambiguating verbal word senses using term weight learning. In our approach, an overlapping clustering algorithm based on Mutual information-based (Mu) term weight learning between 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 problem, for a low Mu value, we distinguish between unobserved co-occurrences that are likely to occur in a new corpus and those that are not, by using similarity-based estimation between two cooccurrences of words. For the results, term weight learning is performed. Parameters of term weighting are then estimated so as to maximise the collocations which characterise every sense and minimise the other collocations. In the following sections, we first define a polysemy from the viewpoint of clustering, then describe how to extract collocations using similaritybased estimation. Next, we present a clustering method and a method for verbal word sense disambiguation using the result of clustering. Finally, we report on an experiment in order to show the effect of the method. Most previous corpus-based WSD algorithms are based on the fact that semantically similar words appear in a similar context. Semantically similar 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 sorting out specifies is likely to take time. (s2') We spent a lot of time and money in building our group of stations. Let us consider a two-dimensional Euclidean space spanned by the two axes, each associated with stake and time, and in which take is assigned a vector whose value of the i-th dimension is the value of Mu between the verb and the noun assigned to the i-th axis. Take co-occurs with the two nouns, while buy and spend co-occur only with one of the two nouns. Therefore, the distances between take and these two verbs are large In order to capture the synonymy of take with the two verbs correctly, one has to decompose the vector assigned to take into two component vectors, takel and take2, each of which corresponds to one of the two distinct usages of take (in Figure   1 ). (we call them hypothetical verbs in the following). The decomposition of a vector into a set of its component vectors requires a proper decomposition of the context in which the word occurs. Furthermore, in a general situation, a polysemous verb co-occurs with a large group of nouns and one has to divide the group of nouns into a set of subgroups, each of which correctly characterises the context for a specific sense of the polysemous word. Therefore, the algorithm has to be able to determine when the context of a word should be divided and how. The approach proposed in this paper explicitly introduces new entities, i.e. hypothetical verbs when an entity is judged polysemous and associates them with contexts which are sub-contexts of the context of the original entity• Our algorithm has two basic operations, splitting and lumping• Splitting means to divide a polysemous verb into two hypothetical verbs and lumping means to combine two hypothetical verbs to make one verb out of them (Fukumoto and Tsujii, 1994) .
REF ) describe a method for discriminating among verb senses based on determining which nouns co-occur with the target verb.
Word Sense Disambiguation In Untagged Text Based On Term Weight Learning
Conference Of The European Association For Computational Linguistics
Computer Science