NLPR at TREC 2005: HARD Experiments

نویسندگان

  • Bibo Lv
  • Jun Zhao
چکیده

1 Overview It is the third time that Chinese Information Processing Group of NLPR takes part in TREC. In the past, we participated in Novelty track and Robust track, in which we had evaluated our two key notions: Window-based Retrieval Algorithm and Result Emerging Strategy [1][2]. This year we focus on investigating the significance of relevance feedback, so HARD track is our best choice. HARD2005 is very different from that in the past two years. Firstly, Metadata is removed from topic description so that the topic description in HARD is the same as that of Robust track. Secondly, passage retrieval is cancelled this year. The paper introduces our work on HARD Track in TREC 2005, mainly (1) we propose a new feature selection method for query expansion in relevance feedback; (2) we adopt some query expansion methods. Our paper is organized as follows. Section 2 introduces our system, a new term selection algorithm for query expansion, and our clarification forms. Section 3 presents our query expansion methods. In section 4 experimental results are given, and finally we conclude our work in section 5. As to the retrieval model, Lemur toolkit developed by UMASS and CMU includes six different retrieval models [3]. In order to facilitate our work, we use Okapi BM25 [4][5] as the retrieval model, which is based on the probability model of Robertson and Sparck Jones. The formula is described as follow: 3 1 2 3 (1) (1) | | k qtf k tf avdl dl w k Q K tf k qtf avdl dl + + − = + ⋅ ⋅ + + + (1) 1 ((1) /) K k b b dl avdl = − + ⋅

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تاریخ انتشار 2005