9. A method comprising: receiving a span of text from a document; receiving a phrase vector for the span, the phrase vector having a quantity of features and representing a context for the span; determining, using at least one silicon-based hardware processor, a quantity of candidate entities from a knowledge base for an ambiguous entity mention included in the span; for each of the quantity of candidate entities: determining, using the at least one silicon-based hardware processor, a support score for the candidate entity for each feature in the phrase vector, combining, using the at least one silicon-based hardware processor, the support scores additively, and computing, using the combined support scores, a probability that the span resolves to the candidate entity given the context; and resolving, using the at least one silicon-based hardware processor, the span to a candidate entity with a highest probability.
14. The method of claim 9 , wherein combining the support scores additively comprises: computing a sum of the support scores for the candidate entity; and dividing the sum by the quantity of features.