A Relational Model of Semantic Similarity between Words using Automatically Extracted Lexical Pattern Clusters from the Web

Semantic similarity is a central concept that extends across numerous fields such as artificial intelligence, natural language processing, cognitive science and psychology. Accurate measurement of semantic similarity between words is essential for various tasks such as, document clustering, information retrieval, and synonym extraction. We propose a novel model of semantic similarity using the semantic relations that exist among words. Given two words, first, we represent the semantic relations that hold between those words using automatically extracted lexical pattern clusters. Next, the semantic similarity between the two words is computed using a Mahalanobis distance measure. We compare the proposed similarity measure against previously proposed semantic similarity measures on Miller-Charles benchmark dataset and WordSimilarity353 collection. The proposed method outperforms all existing web-based semantic similarity measures, achieving a Pearson correlation coefficient of 0.867 on the Millet-Charles dataset.