Affiliations: Department of Computer Science, Faculty of Mathematics
and Computer Science, University of Bucharest, Romania.
fhristea@fmi.unibuc.ro | Department of Computer Science, Faculty of Mathematics
and Computer Science, University of Bucharest, Romania.
mpopescu@phobos.cs.unibuc.ro
Note: [] Address for correspondence: Department of Computer Science,
Faculty of Mathematics and Computer Science, University of Bucharest,
Romania
Abstract: The present paper extends a new word sense disambiguation method [9]
to the case of adjectives. The method lies at the border between unsupervised
and knowledge-based techniques. It performs unsupervised word sense
disambiguation based on an underlying Naïve Bayes model, while using
WordNet as knowledge source for feature selection. The proposed extension of
the disambiguation method makes ample use of the WordNet semantic relations
that are typical of adjectives. Its performance is compared to that of previous
approaches that rely on completely different feature sets. Test results show
that feature selection using a knowledge source of type WordNet is more
effective in the disambiguation of adjective senses than local type features
(like part-of-speech tags) are.
Keywords: word sense disambiguation, unsupervised disambiguation, knowledge-based disambiguation , Bayesian classification, the EM algorithm
