Learning Term Weights for Ad-hoc Retrieval
This work addresses a specific issue in information retrieval for search systems, but appears incremental as it builds on existing learning-to-rank principles without introducing a new paradigm.
The paper tackles the problem of computing term weights for ad-hoc retrieval by proposing a learning-to-rank approach to determine weights based on term occurrence patterns, but does not provide concrete numerical results.
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.