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Parsimonious Language Models for Information Retrieval (2004)
| Content Provider | CiteSeerX |
|---|---|
| Author | Robertson, Stephen Hiemstra, Djoerd Zaragoza, Hugo |
| 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. |
| File Format | |
| Publisher Date | 2004-01-01 |
| Access Restriction | Open |
| Subject Keyword | Retrieval Process Standard Approach Parsimonious Model Experimental Result Parsimonious Language Model Search Time Information Retrieval Feedback Time Standard Model Language Model |
| Content Type | Text |
