JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2016, Vol. 51 ›› Issue (7): 18-22.doi: 10.6040/j.issn.1671-9352.1.2015.031

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Study on collection statistics for parameter selection in pseudo relevance feedback

MENG Ye, ZHANG Peng, SONG Da-wei   

  1. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin 300072, China
  • Received:2015-11-14 Online:2016-07-20 Published:2016-07-27

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Abstract: Pseudo-relevance feedback(PRF)is an effective technique used to improve the Ad hoc retrieval performance. For PRF methods, how to optimize the balance parameter between the original query model and feedback model is an important but difficult problem. In the current feedback methods, the balance parameter is often set to a fixed value across all collections. However, due to the difference among collections, this parameter should be tuned differently. In this paper, we aim to discover some meaningful clues for the optimization of the balance parameter through analyzing the statistical features of collections. We investigates the dependency between the optimal parameter and a number of collection statistics, including the standard deviation of document length(Dev(dl)), the proportion of low frequency terms in the collection(LFT-C)and in the expansion terms. The experiments on six TREC collections demonstrate that the higher LFT-C and Dev(dl)are, the bigger weight of the original query model should be given.

Key words: information retrieval, pseudo-relevance feedback, collection characteristics

CLC Number: 

  • TP393
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