Learning Term Weights for Ad-hoc Retrieval
Information Retrieval
2016-06-15 v1
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.
Keywords
Cite
@article{arxiv.1606.04223,
title = {Learning Term Weights for Ad-hoc Retrieval},
author = {B. Piwowarski},
journal= {arXiv preprint arXiv:1606.04223},
year = {2016}
}