Jaccard coefficient-based word sense disambiguation using hybrid knowledge resources

Word Sense Disambiguation (WSD) has become a popular method for solving the ambiguous meaning of the words in Information Retrieval (IR) field area. Under the Natural Language Processing (NLP) community, WSD has been described as the task which able to select the appropriate meaning among the ambiguous meanings to a given word. Among three approaches, supervised based, unsupervised based and knowledge based approaches to WSD, this paper focuses on both supervised based and knowledge based approaches by proposing new Jaccard coefficient-based WSD algorithm to overcome the vocabulary miss match problem. WordNet and corpus external knowledge resources are utilized as the sense repositories by linking up with the new WSD algorithm to consider additional semantic for WSD. According to sample testing, IR system with new WSD algorithm attains more about 20 percent of total accuracy rate than traditional IR system.

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