Word sense disambiguation (WSD) is a computational linguistics task likely to benefit from the tradition of combining different knowledge sources in artificial in telligence research. An important step in the exploration of this hypothesis is to determine which linguistic knowledge sources are most useful and whether their combination leads to improved results. We present a sense tagger which uses several knowledge sources. Tested accuracy exceeds 94 % on our evaluation corpus. Our system attempts to disambiguate all content words in running text rather than limiting itself to treating a restricted vocabulary of words. It is argued that this approach is more likely to assist the creation of practical systems. 1.

. NLP systems crucially depend on the knowledge structures devoted to describing and representing word senses. Although automatic Word Sense Disambiguation (WSD) is now an established task within empirically-based computational approaches to NLP, the suitability of the available set (and granularity) of senses is still a problem. Application domains exhibit specific behaviors that cannot be fully predicted in advance. Suitable adaptation mechanisms have to be made available to NLP systems to tune existing large scale sense repositories to the practical needs of the target application, such as information extraction or machine translation. In this paper we describe a model of "lexical tuning" --the systematic adaptation of a lexicon to a corpus---that specializes the set of verb senses required for an NLP application, and builds inductively the corresponding lexical descriptions for those senses. 1 Word Sense Disambiguation and Lexical Tuning It is a commonplace observation (and the ...

In this paper we propose algorithms to automatically classify sentences into metaphoric or normal usages. Our algorithms only need the WordNet and bigram counts, and does not require training. We present empirical results on a test set derived from the Master Metaphor List. We also discuss issues that make classification of metaphors a tough problem in general. 1