@article{Bollegala2009,
abstract = {Semantic similarity is a central concept that extends across numerous fields such as artificial intelligence; achieving a Pearson correlation coefficient of 0.867 on the Millet-Charles dataset.; and synonym extraction. We propose a novel model of semantic similarity using the semantic relations that exist among words. Given two words; cognitive science and psychology. Accurate measurement of semantic similarity between words is essential for various tasks such as; document clustering; first; information retrieval; natural language processing; 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 WordSimilarity-353 collection. The proposed method outperforms all existing web-based semantic similarity measures; we represent the semantic relations that hold between those words using automatically extracted lexical pattern clusters. Next},
address = {Morristown, NJ, USA},
annote = {
        From Duplicate 2 ( 
        
        
          A relational model of semantic similarity between words using automatically extracted lexical pattern clusters from the web
        
        
         - Bollegala, Danushka; Matsuo, Yutaka; Ishizuka, Mitsuru )

        
        

        

        

      },
author = {Bollegala, Danushka and Matsuo, Yutaka and Ishizuka, Mitsuru},
doi = {10.3115/1699571.1699617},
isbn = {9781932432626},
journal = {Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 2 - EMNLP '09},
keywords = {SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
pages = {803},
publisher = {Association for Computational Linguistics},
title = {{A relational model of semantic similarity between words using automatically extracted lexical pattern clusters from the web}},
url = {http://portal.acm.org/citation.cfm?doid=1699571.1699617},
year = {2009}
}