Learning Expressive Models for Word Sense Disambiguation

We present a novel approach to the word sense disambiguation problem which makes use of corpus-based evidence combined with background knowledge. Employing an inductive logic programming algorithm, the approach generates expressive disambiguation rules which exploit several knowledge sources and can also model relations between them. The approach is evaluated in two tasks: identification of the correct translation for a set of highly ambiguous verbs in English-Portuguese translation and disambiguation of verbs from the Senseval-3 lexical sample task. The average accuracy obtained for the multilingual task outperforms the other machine learning techniques investigated. In the monolingual task, the approach performs as well as the state-of-the-art systems which reported results for the same set of verbs.