Abstract
We propose a topic based approach to language modelling for ad-hoc Information Retrieval (IR). Many smoothed estimators used for the multinomial query model in IR rely upon the estimated background collection probabilities. In this paper, we propose a topic based language modelling approach, that uses a more informative prior based on the topical content of a document. In our experiments, the proposed model provides comparable IR performance to the standard models, but when combined in a two stage language model, it outperforms all other estimated models.
Original language | English |
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Title of host publication | 2004 IEEE International Joint Conference on Neural Networks |
Place of Publication | Piscataway, NJ |
Publisher | IEEE |
Pages | 3281-3286 |
Number of pages | 6 |
ISBN (Print) | 0780383591 |
DOIs | |
Publication status | Published - 17 Jan 2005 |
Event | 2004 IEEE International Joint Conference on Neural Networks - Budapest, Hungary Duration: 25 Jul 2004 → 29 Jul 2004 |
Conference
Conference | 2004 IEEE International Joint Conference on Neural Networks |
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Country/Territory | Hungary |
City | Budapest |
Period | 25/07/04 → 29/07/04 |
Keywords
- language modelling
- Information retrieval
- two stage language model