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Roberto Navigli Dipartimento di Informatica Sapienza - Universit` di Roma a Via Salaria, 113 - 00198 Roma Italy navigli@di.uniroma1.it Abstract We present a novel graph-based algorithm for the automated disambiguation of glosses in lexical knowledge resources. A dictionary graph is built starting from senses (vertices) and explicit or implicit relations in the dictionary (edges). The approach is based on the identification of edge sequences which constitute cycles in the dictionary graph (possibly with one edge reversed) and relate a source to a target word sense. Experiments are performed on the disambiguation of ambiguous words in the glosses of WordNet and two machine-readable. | Using Cycles and Quasi-Cycles to Disambiguate Dictionary Glosses Roberto Navigli Dipartimento di Informatica Sapienza - Universita di Roma Via Salaria 113 - 00198 Roma Italy navigli@di.uniroma1.it Abstract We present a novel graph-based algorithm for the automated disambiguation of glosses in lexical knowledge resources. A dictionary graph is built starting from senses vertices and explicit or implicit relations in the dictionary edges . The approach is based on the identification of edge sequences which constitute cycles in the dictionary graph possibly with one edge reversed and relate a source to a target word sense. Experiments are performed on the disambiguation of ambiguous words in the glosses of WordNet and two machine-readable dictionaries. 1 Introduction In the last two decades we have witnessed an increasing availability of wide-coverage lexical knowledge resources in electronic format most notably thesauri such as Roget s Thesaurus Ro-get 1911 the Macquarie Thesaurus Bernard 1986 etc. machine-readable dictionaries e.g. the Longman Dictionary of Contemporary English Proctor 1978 computational lexicons e.g. WordNet Fellbaum 1998 etc. The information contained in such resources comprises depending on their kind sense inventories paradigmatic relations e.g. fleshn is a kind of plant tissue 1 text definitions e.g. fleshn is defined as a soft moist part of a fruit usage examples and so on. Unfortunately not all the semantics are made explicit within lexical resources. Even Word-Net the most widespread computational lexicon of English provides explanatory information in the form of textual glosses i.e. strings of text 1We denote as wp the ith sense in a reference dictionary of a word w with part of speech p. which explain the meaning of concepts in terms of possibly ambiguous words. Moreover while computational lexicons like WordNet contain semantically explicit information such as among others hypernymy and meronymy relations most thesauri glossaries and .
