1. A method comprising: matching a token from at least a portion of a text string with a matching concept in an ontology, wherein the at least a portion of the text string has been labeled as corresponding to a particular entity type; identifying a first concept as being hierarchically related to the matching concept within the ontology; identifying a second concept as being hierarchically related to the first concept within the ontology; and training, using at least one processor, a statistical model to associate the first concept with a first probability of corresponding to the particular entity type and the second concept with a second probability of corresponding to the particular entity type, based at least in part on the labeling of the at least a portion of the text string as corresponding to the particular entity type.
1. A method comprising: matching a token from at least a portion of a text string with a matching concept in an ontology, wherein the at least a portion of the text string has been labeled as corresponding to a particular entity type; identifying a first concept as being hierarchically related to the matching concept within the ontology; identifying a second concept as being hierarchically related to the first concept within the ontology; and training, using at least one processor, a statistical model to associate the first concept with a first probability of corresponding to the particular entity type and the second concept with a second probability of corresponding to the particular entity type, based at least in part on the labeling of the at least a portion of the text string as corresponding to the particular entity type. 2. The method of claim 1 , wherein the particular entity type is a particular type of medical fact.