Learning Term Weights for Ad-hoc Retrieval

Benjamin Piwowarski (1)

1 BD - Bases de Données

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.

Domaines: Recherche d'information [cs.IR]

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