In ML estimation,α m estimated from document D is determined asα Thus, ML estimated generative probability of w in the hyperspherical QLMs is formulated as where v is a normalized word embedding of v. In MAP estimation,α m estimated from document D is determined aŝ where C is document collection in which all retrieved documents are included.
wCwV used cosine distance between two document vectors composed by adding word embeddings with IDF weights (Brokos et al., 2016).