Clustering Using Feature Domain Similarity to Discover Word Senses for Adjectives

This paper presents a new clustering algorithm called DSCBC which is designed to automatically discover word senses for polysemous words. DSCBC is an extension of CBC Clustering [11], and incorporates feature domain similarity: the similarity between the features themselves, obtained a priori from sources external to the dataset used at hand. When polysemous words are clustered, words that have similar sense patterns are often grouped together, producing polysemous clusters: a cluster in which features in several different domains are mixed in. By incorporating the feature domain similarity in clustering, DSCBC produces monosemous clusters, thereby discovering individual senses of polysemous words. In this work, we apply the algorithm to English adjectives, and compare the discovered senses against WordNet. The results show significant improvements by our algorithm over other clustering algorithms including CBC.

• Type: Conference
• Year: 2007
• Where: SEMCO
• Authors: Noriko Tomuro, Steven L. Lytinen, Kyoko Kanzaki, Hitoshi Isahara
• Keywords: Clustering Algorithms, Feature Domain Similarity, Polysemous Words, Semantic Computing, SEMCO 2007