Measuring semantic similarity between words is vital for various applications in natural language processing, such as language modeling, information retrieval, and document clustering. We propose a method that utilizes the information available on the Web to measure semantic similarity between a pair of words or entities. We 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 MillerCharles' 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 0.834. Moreover, the proposed semantic similarity measure significantly improves the accuracy (Fmeasure of 0.78) in a named entity clustering task, proving the capability of the proposed measure to capture semantic similarity using web content.
One of the most relevant work is REF , which proposed to integrate various patterns in order to measure semantic similarity between words.
An Integrated Approach to Measuring Semantic Similarity between Words Using Information Available on the Web
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference
Computer Science