Abstract
Word sense disambiguation (WSD) based on supervised machine learning is hard to deal with large-scale WSD because of its big labor cost.To solve this problem,an unsupervised WSD method was provided,which describes the word senses of an ambiguous word via synthesizing multiple knowledge sources in WordNet ontology,including definition glosses,samples,structured semantic relations,domain attributes,etc.From the description,a representative glossary and a domain representative glossary are deduced.The two structures together with the word sense frequency distribution and the context are used for WSD.The average disambiguation accuracy was 49.93% by this method in open test for six representative unsupervised WSD methods with Senseval-3 English lexical sample data set.
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