By experimenting with the other ranking functions and their parameters, we noticed that the implementation of BM25, available in Lemur, provided almost identical performance (0.204).
for each selected (sampled) pair of documents (dr, di ), such that dr is a relevant document and di is irrelevant, the classifier was presented with a positive example created from the vector of differences of features f p = f(q, dr) -f(q, di), and also with the negative example as the inverse of it: f n = f(q, di) -f(q, dr).