Given a contextualized token, the classifier computes the probability of an entity link for each entry in the entity vocabulary.
formally, let d be BERT's token embedding size, and E ∈ R |KB|×d the entity classification layer, with |KB| being the number of entities in the KB, V is the sub-word vocabulary, the probability p(j|v, h) of word v -which is the i-th token in context h -linking to entity j is computed by σ(E j c i ), where σ is the sigmoid function.