UBC-ZAS: a k-NN based multiclassifier system to perform WSD in a reduced dimensional vector space
SemEval@ACL(2007)
摘要
In this article a multiclassifier approach for word sense disambiguation (WSD) problems is presented, where a set of k-NN classifiers is used to predict the category (sense) of each word. In order to combine the predictions generated by the multiclassifier, Bayesian voting is applied. Through all the classification process, a reduced dimensional vector representation obtained by Singular Value Decomposition (SVD) is used. Each word is considered an independent classification problem, and so different parameter setting, selected after a tuning phase, is applied to each word. The approach has been applied to the lexical sample WSD subtask of SemEval 2007 (task 17).
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关键词
reduced dimensional vector space,word sense disambiguation,singular value decomposition,lexical sample wsd subtask,k-nn classifier,different parameter setting,multiclassifier system,reduced dimensional vector representation,classification process,multiclassifier approach,independent classification problem,bayesian voting,vector space
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