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

ACL ID D09-1084
Title A Relational Model of Semantic Similarity between Words using Automatically Extracted Lexical Pattern Clusters from the Web
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

Semantic similarity is a central concept that extends across numerous fields such as artificial intelligence, natural language processing, cognitive science and psychol- ogy. Accurate measurement of semantic similarity between words is essential for various tasks such as, document cluster- ing, 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 seman- tic relations that hold between those words using automatically extracted lexical pat- tern clusters. Next, the semantic similar- ity between the two words is computed using a Mahalanobis distance measure. We compare the proposed similarity mea- sure against previously proposed seman- tic similarity measure...