Discovering Semantic Similarity between Words Using Web Document and Context Aware Semantic Association Ranking
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2013, Vol 2, Issue 6
Abstract
The growth of information in the web is too large, so search engine come to play a more critical role to find relation between input keywords. Semantic Similarity Measure is widely used in Information Retrieval (IR) and also it is important component in various tasks on the web such as relation extraction, community mining, document clustering, and automatic metadata extraction. An empirical method to estimate semantic similarity using page counts and text snippets retrieved from a web search engine for two words. Specifically, define various word co-occurrence measures using page counts and integrate those with lexical patterns extracted from text snippets. Pattern clustering is used to identify the numerous semantic relations that exist between two given words. The optimal combination of page counts-based co-occurrence measures and lexical pattern clusters is learned using support vector machines. The proposed method context Aware Semantic Association Ranking discovering complex and meaningful relationships, which we call Semantic Associations.
Authors and Affiliations
P. Ilakiya
Keywords
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