Combining Unsupervised Lexical Knowledge Methods for Word Sense Disambiguation

Combining Unsupervised Lexical Knowledge Methods for Word Sense Disambiguation * German Rigau, Jordi Atserias Eneko Agirre Dept. de Llenguatges i Sist. Informktics Lengoaia eta Sist. Informatikoak saila Universitat Polit~cnica de Catalunya Euskal Herriko Unibertsitatea Barcelona, Catalonia Donostia, Basque Country {g. rigau, bat alla}@is i. upc. es j ibagbee~s i. ehu. es Abstract This paper presents a method to combine a set of unsupervised algorithms that can accurately disambiguate word senses in a large, completely untagged corpus. Al- though most of the techniques for word sense resolution have been presented as stand-alone, it is our belief that full-fledged lexical ambiguity resolution should com- bine several information sources and tech- niques. The set of techniques have been applied in a combined way to disambiguate the genus terms of two machine-readable dictionaries (MRD), enabling us to con- struct complete taxonomies for Spanish and French. Tested accuracy is above 80% overall and 95% for two-way ambiguous genus terms, showing that taxonomy build- ing is not limited to structured dictionaries such as LDOCE. 1 Introduction While in English the "lexical bottleneck" problem (Briscoe, 1991) seems to be softened (e.g. WordNet (Miller, 1990), Alvey Lexicon (Grover et al., 1993), COMLEX (Grishman et al., 1994), etc.) there are no available wide range lexicons for natural language processing (NLP) for other languages. Manual con- struction of lexicons is the most reliable technique for obtaining structured lexicons but is costly and highly time-consuming. This is the reason for many researchers having focused on the massive acquisi- tion of lexical knowledge and semantic information from pre-existing structured lexical resources as au- tomatically as possible. *This research has been partially funded by CICYT TIC96-1243-C03-02 (ITEM project) and the European Comission LE-4003 (EuroWordNet project). As dictionaries are special texts whose subject matter is a language (or a pair of languages in the case of bilingual dictionaries) they provide a wide range of information about words by giving defini- tions of senses of words, and, doing that, supplying knowledge not just about language, but about the world itself. One of the most important relation to be ex- tracted from machine-readable dictionaries (MRD) is the hyponym/hypernym relation among dictio- nary senses (e.g. (Amsler, 1981), (Vossen and Serail, 1990) ) not only because of its own importance as the backbone of taxonomies, but also because this rela- tion acts as the support of main inheritance mecha- nisms helping, thus, the acquisition of other relations and semantic features (Cohen and Loiselle, 1988), providing formal structure and avoiding redundancy in the lexicon (Briscoe et al., 1990). For instance, following the natural chain of dictionary senses de- scribed in the Diccionario General Ilustrado de la Lengua Espadola (DGILE, 1987) we can discover that a bonsai is a cultivated plant or bush. bonsai_l_2 planta y arbusto asi cultivado. (bonsai, plant and bush cultivated in that way) The hyponym/hypernym relation appears be- tween the entry word (e.g. bonsai) and the genus term, or the core of the phrase (e.g. planta and arbusto). Thus, usually a dictionary definition is written to …

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