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Learning Term Weights for Ad-hoc Retrieval

Benjamin Piwowarski 1
1 BD - Bases de Données
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Most Information Retrieval models compute the relevance score of a document for a given query by summing term weights specific to a document or a query. Heuristic approaches, like TF-IDF, or probabilistic models, like BM25, are used to specify how a term weight is computed. In this paper, we propose to leverage learning-to-rank principles to learn how to compute a term weight for a given document based on the term occurrence pattern.
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Preprints, Working Papers, ...
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Contributor : Benjamin Piwowarski <>
Submitted on : Thursday, September 1, 2016 - 11:39:28 AM
Last modification on : Friday, January 8, 2021 - 5:32:09 PM
Long-term archiving on: : Saturday, December 3, 2016 - 7:32:45 PM


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  • HAL Id : hal-01358682, version 1
  • ARXIV : 1606.04223


Benjamin Piwowarski. Learning Term Weights for Ad-hoc Retrieval. 2016. ⟨hal-01358682⟩



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