Semantic similarity plays a significant role in the areas of Web mining, Information Retrieval, NLP and Text mining. Even though it is exploited in various applications accurately measuring semantic similarity still remains a challenging task. In this paper a method is proposed to measure semantic similarity between words using web as information source and by combining two existing approaches to measure semantic similarity they are: Semantic Word Distance (SWD) and Snippets. The SWD measure finds the semantic similarity by determining the frequency of occurrences of the words in web pages (corpus). The semantic relation between words are also obtained through lexical patterns which are extracted from text snippets. A robust method is used to integrate these similarity scores using support vector machine. For the experimental purpose 100 word pairs are used to train the support vector machine and it classifies the word pair as either synonymous or non synonymous with higher accuracy.

1. Basu, T. and Murthy, C. A. (2009) 'Semantic Relation between words with the web as Information source', PReMI- Proc of the 3rd International Conference on Pattern Recognition and Machine Intelligence, LNCS 5909, pp. 267-272.
2. Bollegala,D. , Matsuo,Y. , and Ishizuka, M. (2011), 'A Web Search Engine Based Approach to Measure Semantic Similarity between Words', IEEE Transactions on Knowledge and Data Engineering, Vol 23, NO 7, pp. 977-990.
3. Cilibrasi, R. and Vitanyi, P. (2007) 'The google Similarity distance', IEEE Tansactions on Knowledge and Data Engineering, pp. 370-383.
4. Jinwu HU. , Liuling DAI, Bin LIU, (2008) ' Measure Semantic Similarity between english words', ICYCS- The 9th International Conference for Young Computer Scientists, pp. 1689-1694.
5. Liu, B. , Dai, L. Xia, Y. and Wu, S. (2008) ' Measuring semantic similarity between words using How net', ICCSI - International conference on Computer Science and Information Technology, pp. 601-605.
6. Mehmet Ali Salahli (2009) ' An approach for measuring semantic relatedness between words via related terms', Mathematical and Computational Applications, Vol. 14, No. 1, pp. 55-63.
7. Pedersen, T. , Patwardhan, S. and Michelizzi, J. (2004) 'WordNet::Similarity - Measuring the Relatedness of Concepts', HLT-NAACL Demonstration papers, pp: 38-41.
8. Sahami, M. and Heilman, T. (2006) 'A Web-based Kernal Function for Measuring the Similarity of Short Text Snippets', Proc of 15th Int'l World Wide Web Conf. pp. 377-386.
9. Takale, S. A. and Nandgaonkar, S. A (2010) 'Measuring semantic similarity between words using web documents', IJASCA- International Journal of Advanced Computer Science and Applications, Vol 1, No. 4,pp. 78-82.
10. Zhiqiang, L. , Werimin, S. , and Zhenhua, Y. (2009), 'Measuring Semantic Similarity between Words Using Wikipedia', WISM - International Conference on Web Information Systems and Mining, pp: 251-255.

Page Count Measures Semantic Similarity Semantic Word Distance Snippet