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Measuring semantic similarity between words is vital for various applications in natural language processing, such as language modeling, information retrieval, and document clustering. This method that utilizes the information available on the Web to measure semantic similarity between a pair of words or entities and also integrate page counts for each word in the pair and lexico-syntactic patterns that occur among the top ranking snippets for the AND query using support vector machines. Experimental results on Miller-Charles' benchmark data set show that the proposed measure outperforms all the existing web based semantic similarity measures by a wide margin, achieving a correlation coefficient of 0.8129.
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