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摘要:
Concept similarity measure based on feature vector has wide application in various fields, but the problems of polysemy and synonym existing in feature vector affect the similarity measure. We present a feature optimization algorithm based on Chinese Wikipedia which can reduces this effect. First we build a POS feature dictionary (POS-Dic) and a POS Tongyici Cilin(POS-Cilin), and then a new feature vector is used for concept similarity measure. Experiments show that the algorithm effectively reduces the influence of polysemy and synonym on the concept similarity measure. © 2017 IEEE.
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年份: 2017
页码: 2174-2179
语种: 英文
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