期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2013
卷号：10
期号：2
出版社：IJCSI Press
摘要：Semantic similarity is a way of analyzing the perfect synonym that exists between word-pairs. This measure is necessary to detect the degree of relationship that persists within word-pairs. To compute the semantic similarity that lies between a word-pair, clustering and classification augmented with semantic similarity (CCASS) was developed. CCASS is a novel method that uses page counts and text snippets returned by search engine. Several similarity measures are defined using the page counts of word-pairs. Lexical pattern clustering is applied on text snippets, obtained from search engine. These are fed to the support vector machine (SVM) which computes the semantic similarity that exists between word-pairs. Based on this value obtained from the support vector machine, Simple KMeans clustering algorithm is used to form clusters. Upcoming word-pairs can be classified, after computation of its semantic similarity measure. If it does match with the existing clusters, a new cluster may be created.
关键词：Semantic Similarity; Similarity measure; Clustering; Classification; Text mining.
