Estimation of query model from parsimonious translation model
SCIE
SCOPUS
- Title
- Estimation of query model from parsimonious translation model
- Authors
- Na, SH; Kang, IS; Kang, SJ; Lee, JH
- Date Issued
- 2005-01
- Publisher
- SPRINGER-VERLAG BERLIN
- Abstract
- The KL divergence framework, the extended language modeling approach, have a critical problem with estimation of query model, which is the probabilistic model that encodes user's information need. However, at initial retrieval, it is difficult to expand query model using co-occurrence, because the two-dimensional matrix information such as term co-occurrence must be constructed in offline. Especially in large collection, constructing such large matrix of term co-occurrences prohibitively increases time and space complexity. This paper proposes an effective method to construct co-occurrence statistics by employing parsimonious translation model. Parsimonious translation model is a compact version of translation model, and it contains very small number of parameters that includes non-zero probabilities. Parsimonious translation model enables us to enormously reduce the number of remaining terms in document so that co-occurrence statistics can be calculated in tractable time. In experimentations, the results show that query model derived from parsimonious translation model significantly improves baseline language modeling performance.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/24605
- DOI
- 10.1007/978-3-540-31871-2_21
- ISSN
- 0302-9743
- Article Type
- Article
- Citation
- LECTURE NOTES IN COMPUTER SCIENCE, vol. 3411, page. 239 - 250, 2005-01
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