Comparing and Combining Query Expansion Approaches based on Local and Web Documents
Date Issued
2009
Date
2009
Author(s)
Chien-Hung, Chen
Abstract
Query expansion is an important technique to improve search capability in information retrieval. According to expansion collection, there are two types of query expansion. One is query expansion performed on local documents and another is performed on web documents. The previous research found the method which is based on local documents has bottleneck on poorly performing topics, called hard topics. However, others propose to improve poorly performing topics is exploiting text collections other than the target collection such as the web. Regarding our comparison of these two types of query expansion, our result shows query expansion based on web resource, which is Wikipedia, indeed has better performance on hard topics. As for query expansion performed on local documents, it has better performance on other topics. Therefore, we propose a combined method to integrate two ranked lists of terms expanded by these two types of query expansion, and evaluate the corresponding search performance. Roughly speaking, our combined query expansion methods produce better performance. However, to view it in a strict way, our methods provide balanced results.
Subjects
Blind Relevance Feedback
Query Expansion
Wikipedia
Type
thesis
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