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Feeding Syntactic Versus Semantic Knowledge to a Knowledge-lean Unsupervised Word Sense Disambiguation Algorithm with an Underlying Naive Bayes Model

Hristea F., Colhon M.

Fundamenta Informaticae

2012

Vol. 119, nr 1

61-86

The present paper concentrates on the issue of feature selection for unsupervised word sense disambiguation (WSD) performed with an underlying Na¨ýve Bayes model. It introduces dependency-based feature selection which, to our knowledge, is used for the first time in conjunction with the Na¨ýve Bayes model acting as clustering technique. Construction of the dependency-based semantic space required for the proposed task is discussed. The resulting disambiguation method, representing an extension of the method introduced in [15], lies at the border between unsupervised and knowledge-based techniques. Syntactic knowledge provided by dependency relations (and exemplified in the case of adjectives) is hereby compared to semantic knowledge offered by the semantic network WordNet (and examined in [15]). Our conclusion is that the Na¨ýve Bayes model reacts well in the presence of syntactic knowledge of this type and that dependency-based feature selection is a reliable alternative to the WordNet-based semantic one.

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

Hristea F., Popescu M.

Fundamenta Informaticae

2009

Vol. 91, nr 3-4

547-562

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 Näive 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.