Search

When searching for information a user wants, search engines often return lots of results unintended by the user. Query expansion is a promising approach to solve this problem. In the query expansion research, one of big issues is to generate appropriate keywords representing the user's intention. This paper proposes the related word extraction algorithm (RWEA) which pays attention to the distance between sentences where keywords in the original query appear. The RWEA is based on the idea that a word nearby important words is also important. We conducted several experiments to evaluate the RWEA. The results promise the effectiveness of the RWEA for improving search results.

Search engines give so many results that a user cannot handle them all in a short period of time. Many approaches have been proposed to alleviate this problem. For example, some approaches try to add personalized features and some try to group the results into different categories. The latter one is called a clustering engine, which is the emphasis of this paper. It first reviews several existing approaches such as STC, SHOC, LINGO and SnakeT. It then gives a new approach called HICSEC (Hierarchical Clustering Search Engine with Correlation) to improve the accuracy of clustering by the Correlation calculated with the Singular Value Decomposition.