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Table 1 Features for general learning-to-rank model.

From: Learning to rank diversified results for biomedical information retrieval from multiple features

Feature Description
TF-IDF Term frequency inverse document frequency.
BM25 Okapi BM25 model [21].
DFR BM25 The DFR version of BM25 [23].
InL2 An algorithm derived from the divergence from randomness (DFR) framework [23].
DLH13 An DLH hyper-geometric DFR model (parameter free) [23].
DirKL KL-divergence language model with Dirichlet smoothing [22].
Hiemstra LM Hiemstra's language model [24].
ProxQT Proximity of Query Terms: Intuitively, the more close the query terms occur in a document, the more likely the document would be relevant [25].