International Journal of Computer Science Issues

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Semantic Similarity measures plays an important and significant role in information retrieval, natural language processing and various tasks on web such as relation extraction, community mining, document clustering, and automatic meta-data extraction. In this paper, we have proposed a Modified Pattern Extraction Algorithm [MPEA] to compute the semantic similarity measure between the words by combining both page count method and web snippets method. Four association measures are used to find semantic similarity between words in page count method using web search engines. We use a Sequential Minimal Optimization (SMO) Support Vector Machines (SVM) to find the optimal combination of page counts-based similarity scores and top-ranking patterns from the web snippets method. The SVM is trained to classify the synonymous word-pairs and non-synonymous word-pairs. The proposed approach aims to improve the Correlation values, Precision, Recall, and F-measures, compared to the existing methods. The proposed algorithm outperforms by 89.8 % of correlation value for Miller-Charles dataset and 75.3% of correlation value for Word Similarity dataset.