Adjective Sense Disambiguation at the Border Between Unsupervised and Knowledge-Based Techniques

Article type: Research Article

Authors: Hristea, Florentina | Popescu, Marius

Affiliations: Department of Computer Science, Faculty of Mathematics and Computer Science, University of Bucharest, Romania. [email protected] | Department of Computer Science, Faculty of Mathematics and Computer Science, University of Bucharest, Romania. [email protected]

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

DOI: 10.3233/FI-2009-0057

Journal: Fundamenta Informaticae, vol. 91, no. 3-4, pp. 547-562, 2009