Abstract
We systematically investigate a new approach to estimating the parameters of language models for information retrieval, called parsimonious language models. Parsimonious language models explicitly address the relation between levels of language models that are typically used for smoothing. As such, they need fewer (non-zero) parameters to describe the data. We apply parsimonious models at three stages of the retrieval process: 1) at indexing time; 2) at search time; 3) at feedback time. Experimental results show that we are able to build models that are significantly smaller than standard models, but that still perform at least as well as the standard approaches.
Original language | Undefined |
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Title of host publication | Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval |
Place of Publication | New York, NY, USA |
Publisher | ACM Press |
Pages | 178-185 |
Number of pages | 8 |
ISBN (Print) | 1-58113-881-4 |
DOIs | |
Publication status | Published - Jul 2004 |
Event | 27th Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2004 - Sheffield, United Kingdom Duration: 25 Jul 2004 → 29 Jul 2004 Conference number: 27 |
Publication series
Name | |
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Publisher | ACM Press |
Conference
Conference | 27th Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2004 |
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Abbreviated title | SIGIR |
Country/Territory | United Kingdom |
City | Sheffield |
Period | 25/07/04 → 29/07/04 |
Keywords
- IR-58635
- DB-IR: INFORMATION RETRIEVAL
- EWI-7256
- METIS-221519