Improving relevance feedback in language modeling approach: maximum a posteriori probability criterion and three-component mixture model

Authors:
Seung-Hoon Na;In-Su Kang;Jong-Hyeok Lee
Affiliations:
Div. of Electrical and Computer Engineering, Pohang University of Science and Technology (POSTECH), Advanced Information Technology Research Center (AITrc);Div. of Electrical and Computer Engineering, Pohang University of Science and Technology (POSTECH), Advanced Information Technology Research Center (AITrc);Div. of Electrical and Computer Engineering, Pohang University of Science and Technology (POSTECH), Advanced Information Technology Research Center (AITrc)
Venue:
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Year:
2004

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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.