  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.
