Word Sense Disambiguation using a dictionary for sense similarity measure

Word Sense Disambiguation using a dictionary for sense similarity measure Bruno Gaume Nabil Hathout Philippe Muller IRIT – CNRS, UPS & INPT ERSS – CNRS & UTM IRIT – CNRS, UPS & INPT Toulouse, France Toulouse, France Toulouse, France gaume@irit.fr hathout@univ-tlse2.fr muller@irit.fr Abstract This paper presents a disambiguation method in which word senses are deter- mined using a dictionary. We use a seman- tic proximity measure between words in the dictionary, taking into account the whole topology of the dictionary, seen as a graph on its entries. We have tested the method on the problem of disambiguation of the dic- tionary entries themselves, with promising results considering we do not use any prior annotated data. 1 Introduction Various tasks dealing with natural language data have to cope with the numerous different senses possessed by every lexical item: ma- chine translation, information retrieval, infor- mation extraction ... This very old issue is far from being solved, and evaluation of methods addressing it is far from obvious (Resnik and Yarowsky, 2000). This problem has been tack- led in a number of ways1: by looking at con- texts of use (with supervised learning or un- supervised sense clustering) or by using lexi- cal resources such as dictionaries or thesauri. The first kind of approach relies on data that are hard to collect (supervised) or very sensitive to the type of corpus (unsupervised). The sec- ond kind of approach tries to exploit the lexical knowledge that is represented in dictionaries or thesaurus, with various results from its incep- tion up to now (Lesk, 1986; Banerjee and Ped- ersen, 2003). In all cases, a distance between words or word senses is used as a way to find the right sense in a given context. Dictionary- based approaches usually rely on a comparison of the set of words used in sense definitions and 1A good introduction is (Ide and Véronis, 1998), or (Manning and Schütze, 1999), chap. 7. in the context to disambiguate2. This paper presents an algorithm which uses a dictionary as a network of lexical items (cf. sections 2 and 3) to compute a semantic simi- larity measure between words and word senses. It takes into account the whole topology of the dictionary instead of just the entry of target words. This arguably gives a certain robustness of the results with respect to the dictionary. We have begun testing this approach on word sense disambiguation on definitions of the dictionary itself (section 5), but the method is expected to be more general, although this has not been tested yet. Preliminary results are quite encour- aging considering that the method does not re- quire any prior annotated data, while operating on an unconstrained vocabulary. 2 Building the graph of a dictionary The experiment we describe here has been achieved on a dictionary restricted to nouns and verbs only: we considered dictionary entries classified as nouns and verbs and noun and verb lemmas occurring within those entries. The basic idea is to consider the dictionary as …

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