Probabilistic network models for word sense disambiguation

Authors:
Gerald Chao;Michael G. Dyer
Affiliations:
University of California, Los Angeles, California;University of California, Los Angeles, California
Venue:
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Year:
2001

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Abstract

We present the techniques used in the word sense disambiguation (WSD) system that was submitted to the Senseval-2 workshop. The system builds a probabilistic network per sentence to model the dependencies between the words within the sentence, and the sense tagging for the entire sentence is computed by performing a query over the network. The salient context used for disambiguation is based on sentential structure and not positional information. The parameters are established automatically and smoothed via training data, which was compiled from the SemCor corpus and the WordNet glosses. Lastly, the One-sense-per-discourse (OSPD) hypothesis is incorporated to test its effectiveness. The results from two parameterization techniques and the effects of the OSPD hypothesis are presented.