Word Sense Disambiguation using Statistical Models and WordNet

Antonio Molina (Departament de Sistemes Inform`atics i Computaci´o Universitat Polit`ecnica de Val`encia (Spain))

Ferran Pla (Departament de Sistemes Inform`atics i Computaci´o Universitat Polit`ecnica de Val`encia (Spain))

Encarna Segarra (Departament de Sistemes Inform`atics i Computaci´o Universitat Polit`ecnica de Val`encia (Spain))

Lidia Moreno (Departament de Sistemes Inform`atics i Computaci´o Universitat Polit`ecnica de Val`encia (Spain))

WP3: Tools & Components

One of the main problems in Natural Language Processing is lexical ambiguity, words often have multiple lexical functionalities (i.e. they can have various parts-of-speech) or have several semantic meanings. Nowadays, the semantic ambiguity problem, most known asWord Sense Disambiguation, is still an open problem in this area. The accuracy of the different approaches for semantic disambiguation is much lower than the accuracy of the systems which solve other kinds of ambiguity, such as part-of-speech tagging. Corpus-based approaches have been widely used in nearly all natural language processing tasks. In this work, we propose a Word Sense Disambiguation system which is based on Hidden Markov Models and the use of WordNet. Some experimental results of our system on the SemCor corpus are provided.

WordNet, Word sense disambiguation

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