1. A computer system comprising: at least one processor; and memory storing: a graph-structured knowledge base of entities connected by relationships, and instructions that, when executed by the at least one processor, causes the computer system to perform operations comprising: receiving a span of text from a document and a quantity of phrases from the document for the span, the phrases representing a context for the span, determining that the span refers to a quantity of candidate entities from the knowledge base, for each of the quantity of candidate entities: providing the entity and the phrases as input to an additive context model, the context model having been trained to provide a support score for an entity-phrase pair, receiving one or more support scores from the additive context model for the entity, computing a first probability for the entity by adding the support scores together and dividing by the quantity of phrases, the first probability representing a likelihood that the context resolves to the entity, receiving a second probability representing a likelihood that the span resolves to the entity regardless of context, and computing a third probability for the entity by combining the first probability with the second probability, and resolving the span to an entity that has a highest third probability.
1. A computer system comprising: at least one processor; and memory storing: a graph-structured knowledge base of entities connected by relationships, and instructions that, when executed by the at least one processor, causes the computer system to perform operations comprising: receiving a span of text from a document and a quantity of phrases from the document for the span, the phrases representing a context for the span, determining that the span refers to a quantity of candidate entities from the knowledge base, for each of the quantity of candidate entities: providing the entity and the phrases as input to an additive context model, the context model having been trained to provide a support score for an entity-phrase pair, receiving one or more support scores from the additive context model for the entity, computing a first probability for the entity by adding the support scores together and dividing by the quantity of phrases, the first probability representing a likelihood that the context resolves to the entity, receiving a second probability representing a likelihood that the span resolves to the entity regardless of context, and computing a third probability for the entity by combining the first probability with the second probability, and resolving the span to an entity that has a highest third probability. 4. The system of claim 1 , wherein a phrase in the quantity of phrases is a noun phrase from the document.