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Improving Relevance Feedback in Language Modeling Approach: Maximum a Posteriori Probability Criterion and Three-Component Mixture Model

  • Seung-Hoon Na
  • In-Su Kang
  • Jong-Hyeok Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3248)

Abstract

Recently, researchers have tried to extend a language modeling approach to apply relevance feedback. Their approaches can be classified into two categories. One typical approach is the expansion-based feedback that sequentially performs ‘term selection’ and ‘term re-weighting’ separately. Another approach is the model-based feedback that focuses on estimating ‘query language model’, which predicts well users’ information need. This paper improves these two approaches of relevance feedback by using a maximum a posteriori probability criterion, and a three-component mixture model. A maximum a posteriori probability criterion is a criterion for selection of good expansion terms from feedback documents. A three-component mixture model is the method that eliminates the noise of the query language model by adding a ‘document specific topic model’. The experimental results show that our methods increase the precision of relevance feedback for a short length query. In addition, we make some comparative study between several relevance feedbacks in three document collections.

Keywords

Information Retrieval Relevance Feedback Query Term Document Model Feedback Document 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Seung-Hoon Na
    • 1
  • In-Su Kang
    • 1
  • Jong-Hyeok Lee
    • 1
  1. 1.Div. of Electrical and Computer EngineeringPohang University of Science and Technology (POSTECH), Advanced Information Technology Research Center (AITrc) 

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