Improving relevance feedback in language modeling approach: Maximum a posteriori probability criterion and three-component mixture model
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- Title
- Improving relevance feedback in language modeling approach: Maximum a posteriori probability criterion and three-component mixture model
- Authors
- Na, SH; Kang, IS; Lee, JH
- Date Issued
- 2005-01
- Publisher
- SPRINGER-VERLAG BERLIN
- 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.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/24662
- DOI
- 10.1007/978-3-540-30211-7_14
- ISSN
- 0302-9743
- Article Type
- Article
- Citation
- LECTURE NOTES IN COMPUTER SCIENCE, vol. 3248, page. 130 - 138, 2005-01
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