Vector-Based Unsupervised Word Sense Disambiguation for Large Number of Contexts
This paper presents a possible improvement of unsupervised word sense disambiguation (WSD) systems by extending the number of contexts applied by the discrimination algorithms. We carried out an experiment for several WSD algorithms based on the vector space model with the help of the SenseClusters () toolkit. Performances of algorithms were evaluated on a standard benchmark, on the nouns of the Senseval-3 English lexical-sample task (). Paragraphs from the British National Corpus were added to the contexts of Senseval-3 data in order to increase the number of contexts used by the discrimination algorithms. After parameter optimization on Senseval-2 English lexical sample data performance measures show slight improvement, and the optimized algorithm is competitive with the best unsupervised WSD systems evaluated on the same data, such as .
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