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Preprints, Working Papers, ... Year : 2016

Learning Term Weights for Ad-hoc Retrieval

Benjamin Piwowarski

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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hal-01358682 , version 1 (01-09-2016)

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