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THUIR at TREC 2008: Relevance Feedback Track.

Bo Ru Zhou, Fang Qi, Rongwei Cen, Min Zhang, Yiqun Liu, Shaoping Ma

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Abstract

Abstract. Our group has participated into Relevance Feedback (RF) Track in TREC2008. In our experiments, two kinds of techniques, query expansion and search result re-ranking based on document relevance model, are adopted to improve the retrieval performance. The TMiner search engine, from IR group of Tsinghua University, is used as our text retrieval system. 1.

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What this paper is about

Abstract. Our group has participated into Relevance Feedback (RF) Track in TREC2008. In our experiments, two kinds of techniques, query expansion and search result re-ranking based on document relevance model, are adopted to improve the retrieval performance. The TMiner search engine, from IR group of Tsinghua University, is used as our text retrieval system. 1.

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Available abstract

Abstract. Our group has participated into Relevance Feedback (RF) Track in TREC2008. In our experiments, two kinds of techniques, query expansion and search result re-ranking based on document relevance model, are adopted to improve the retrieval performance. The TMiner search engine, from IR group of Tsinghua University, is used as our text retrieval system. 1.

Key concepts: Relevance (law), Relevance feedback, Ranking (information retrieval), Information retrieval, Track (disk drive), Computer science, Query expansion, Search engine

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