Word Sense Disambiguation using Conceptual Density

Word Sense Disambiguation using Conceptual Density Eneko Agirre* Lengoaia eta Sistema Informatikoak saila. Euskal Herriko Universitatea. p.k. 649, 200800 Donostia. Spain. jibagbee@si.heu.es German Rigau** Departament de Llenguatges i Sistemes Informhtics. Universitat Polit~cnica de Catalunya. Pau Gargallo 5, 08028 Barcelona. Spain. g.rigau@lsi.upc.es Abstract. This paper presents a method for the resolution of lexical ambiguity of nouns and its automatic evaluation over the Brown Corpus. The method relies on the use oil' the wide-coverage noun taxonomy of WordNet and the notion of conceptual distance among concepts, captured by a Conceptual Density formula developed for this purpose. This fully automatic method requires no hand coding of lexical entries, hand tagging of text nor any kind of training process. The results of the experiments have been automatically evaluated against SemCor, the sense-tagged version of the Brown Corpus. 1 Introduction Much of recent work in lexical ambiguity resolution offers the prospect that a disambiguation system might be able to receive as input unrestricted text and tag each word with the most likely sense with fairly reasonable accuracy and efficiency. The most extended approach use the context of the word to be disambiguatcd together with inl'ormation about each of its word senses to solve this problem. Interesting experiments have been performed in recent years using preexisting lexical knowledge resources: [Cowie el al. 92], [Wilks et al. 93] with LDOCE, [Yarowsky 92] with Roget's International Thesaurus, and [Sussna 93], [Voorhees 9311, [Richardson et al. 94], [Resnik 95] with WordNet. Although each of these techniques looks promising for disambiguation, either they have been only applied to a small number of words, a few sentences or not in a public domain corpus. For this reason we have tried to disambiguate all the nouns from real *Eneko Agirre was supported by a grant from the Basque Goverment. Part of this work is included in projects 141226-TA248/95 of the Basque Country University and PI95-054 of the Basque Government. **German Rigau was supported by a grant from the Ministerio de Educaci6n y Ciencia. texts in the public domain sense tagged version of the Brown corpus [Francis & Kucera 67], [Miller et al. 93], also called Semantic Concordance or SemCor for short 1, The words in SemCor are tagged with word senses from WordNet, a broad semantic taxonomy for English [Miller 90] 2. Thus, SemCor provides an appropriate environment for testing our procedures and comparing among alternatives in a fully automatic way. The automatic decision procedure for lexical ambiguity resolution presented in this paper is based on an elaboration of the conceptual distance among concepts: Conceptual Density [Agirre & Rigau 95]. Thc system needs to know how words are clustered in semantic classes, and how semantic classes are hierarchically organised. For this purpose, we have used WordNet. Our system tries to resolve the lexical ambiguity ot' nouns by finding the combination of senses from a set of contiguous nouns that maximises the Conceptual Density among senses. The perlbrmance of the procedure was tested on four SemCor texts chosen at random. For comparison purposes …

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