An Algorithm for Search Result Clustering Based on Semantic Relevance Between Words
Guoying Zhang · Journal of Zhengzhou University · 2009
To automatically group search results into thematic clusters has become an important topic in the search engine research area,which can be used to improve the quality of searching service.Since the search engine only returns a ranked list of documents along with their partial content(snippets),simply porting the well known generic algorithms does not work well,because the amount of data for the clustering algorithm is often extremely small and low-quality.An algorithm for search result clustering based on semantic relevance between words is proposed.Word is the clustering elements rather than snippets,and its attributes are the snippets where it appears.To fully explore the semantic relevance between words,the snippets are clustered according to the graph of the semantic relevance.The cluster name can be given once the cluster algorithm is finished.The experiment shows that the algorithm performs better than K-Means and STC.