Calculating the similarity between words and sentences using a lexical database and corpus statistics.

Calculating the semantic similarity between sentences is a long dealt problemin the area of natural language processing. The semantic analysis field has acrucial role to play in the research related to the text analytics. Thesemantic similarity differs as the domain of operation differs. In this paper,we present a methodology which deals with this issue by incorporating semanticsimilarity and corpus statistics. To calculate the semantic similarity betweenwords and sentences, the proposed method follows an edge-based approach using alexical database. The methodology can be applied in a variety of domains. Themethodology has been tested on both benchmark standards and mean humansimilarity dataset. When tested on these two datasets, it gives highestcorrelation value for both word and sentence similarity outperforming othersimilar models. For word similarity, we obtained Pearson correlationcoefficient of 0.8753 and for sentence similarity, the correlation obtained is0.8794.