Weakly Supervised Relevance Feedback based on an Improved Language Model
Relevance feedback, which traditionally uses the terms in the relevant documents to enrich the user’s initial query, is an effective method for improving retrieval performance. This approach has another problem is that Relevance feedback assumes that most frequent terms in the feedback documents are useful for the retrieval. In fact, the reports of some experiments show that it does not hold in reality many expansion terms identified in traditional approaches are indeed unrelated to the query and harmful to the retrieval. In this paper, we propose to select better and more relevant documents with a clustering algorithm. And then we present an improved Language Model to help us identify the good terms from those relevant documents. Ours experiments on the 2008 TREC collection show that retrieval effectiveness can be much improved when the improved Language Model is used.
Information retrieval (IR) relevance feedback cluster,relevant documents query expansion
XIN-SHENG LI SI LI WEI-RAN XU GUANG CHEN JUN GUO
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
国际会议
北京
英文
1-5
2010-08-21（万方平台首次上网日期，不代表论文的发表时间）