Abstract
Web-based association measure aims to evaluate the semantic similarity between two queries (i.e. words or entities) by leveraging the search results returned by search engines. Existing web-relevance similarity measure usually considers all search results for a query as a coarse-grained single topic and measures the similarity between the term vectors constructed by concatenating all search results into a single document for each query. This paper proposes a novel association measure named WSRCM based on web search results clustering and matching to evaluate the semantic similarity between two queries at a fine-grained level. WSRCM first discovers the subtopics in the search results for each query and then measures the consistency between the sets of subtopics for two queries. Each subtopic for a query is expected to describe a unique facet of the query, and two queries sharing more subtopics are deemed more semantically related. Experimental results demonstrate the encouraging performance of the proposed measure.
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Wan, X., Xiao, J. (2009). Towards a Novel Association Measure via Web Search Results Mining. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_83
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DOI: https://doi.org/10.1007/978-3-642-01307-2_83
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