Methods and apparatus for entity detection

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.

3. The method of claim 1 , wherein the first concept is an ancestor of the matching concept within the ontology.

4. The method of claim 3 , wherein the second concept is an ancestor of the first concept within the ontology.

5. The method of claim 4 , wherein the first concept is a parent concept of the matching concept within the ontology, and wherein the second concept is a parent concept of the first concept within the ontology.

6. The method of claim 1 , wherein the matching concept represents a semantic meaning of the token.

7. The method of claim 1 , further comprising training the statistical model to weight the first and second concepts as features of the token labeled as corresponding to the particular entity type, relative to other features of the token.

8. The method of claim 1 , further comprising training the statistical model to optimize a likelihood of labeling the at least a portion of the text string as corresponding to the particular entity type, based at least in part on the first and second concepts as features of the token.

9. Apparatus comprising:

at least one processor; and

at least one processor-readable storage medium storing processor-executable instructions that, when executed by the at least one processor, perform 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 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.

10. The apparatus of claim 9 , wherein the particular entity type is a particular type of medical fact.

11. The apparatus of claim 9 , wherein the first concept is a parent concept of the matching concept within the ontology, and wherein the second concept is a parent concept of the first concept within the ontology.

12. The apparatus of claim 9 , wherein the method further comprises training the statistical model to optimize a likelihood of labeling the at least a portion of the text string as corresponding to the particular entity type, based at least in part on the first and second concepts as features of the token.

13. At least one computer-readable storage medium encoded with computer-executable instructions that, when executed, perform 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 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.

14. The at least one computer-readable storage medium of claim 13 , wherein the particular entity type is a particular type of medical fact.

15. The at least one computer-readable storage medium of claim 13 , wherein the first concept is an ancestor of the matching concept within the ontology.

16. The at least one computer-readable storage medium of claim 15 , wherein the second concept is an ancestor of the first concept within the ontology.

17. The at least one computer-readable storage medium of claim 16 , wherein the first concept is a parent concept of the matching concept within the ontology, and wherein the second concept is a parent concept of the first concept within the ontology.

18. The at least one computer-readable storage medium of claim 13 , wherein the matching concept represents a semantic meaning of the token.

19. The at least one computer-readable storage medium of claim 13 , wherein the method further comprises training the statistical model to weight the first and second concepts as features of the token labeled as corresponding to the particular entity type, relative to other features of the token.

20. The at least one computer-readable storage medium of claim 13 , wherein the method further comprises training the statistical model to optimize a likelihood of labeling the at least a portion of the text string as corresponding to the particular entity type, based at least in part on the first and second concepts as features of the token.