Parsimonious Language Models for Information Retrieval


Hiemstra, Djoerd and Robertson, Stephen and Zaragoza, Hugo (2004) Parsimonious Language Models for Information Retrieval. In: SIGIR’04, July 25–29, 2004, Sheffield, South Yorkshire, UK (pp. pp. 178-185).

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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.
Item Type:Conference or Workshop Item
Copyright:© 2004 ACM
Electrical Engineering, Mathematics and Computer Science (EEMCS)
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