Discriminating among word senses using McQuitty's similarity analysis

Authors:
Amruta Purandare
Affiliations:
University of Minnesota, Duluth, MN
Venue:
NAACLstudent '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Proceedings of the HLT-NAACL 2003 student research workshop - Volume 3
Year:
2003

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Abstract

This paper presents an unsupervised method for discriminating among the senses of a given target word based on the context in which it occurs. Instances of a word that occur in similar contexts are grouped together via McQuitty's Similarity Analysis, an agglomerative clustering algorithm. The context in which a target word occurs is represented by surface lexical features such as unigrams, bigrams, and second order co-occurrences. This paper summarizes our approach, and describes the results of a preliminary evaluation we have carried out using data from the SENSEVAL-2 English lexical sample and the line corpus.