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Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between them is based on the likeness of their meaning or semantic content which can be estimated regarding their syntactical representation. Its applications are Biomedical informatics, GeoInformatics, Computational linguistics, Natural language processing. There are two approaches to compute word similarity, on either using of thesaurus (e.g., Word Net) or statistic from large corpus.PMI means point wise mutual information. It is used to measure semantic similarity. In the existing system PMI and PMImax is used to measure semantic similarity. PMImax is used to find out the maximum correlation between the words. PMImax only find siblings concept but it fails to find out cousin concept. So proposed system uses PPMIC to find and improve word similarity.

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C. Mani, & S. Sakthi. (2017). Improving word similarity by using ppmic with estimates of word polysemy . International Journal of Intellectual Advancements and Research in Engineering Computations, 5(2), 1511–1515. Retrieved from