Probabilistic Reasoning for Entity & Relation Recognition

This paper develops a method for recognizing relations and entities in sentences, while taking mutual dependencies among them into account. E.g., the kill (Johns, Oswald) relation in: “J. V. Oswald was murdered at JFK after his assassin, K. F. Johns...” depends on identifying Oswald and Johns as people, JFK being identified as a location, and the kill relation between Oswald and Johns; this, in turn, enforces that Oswald and Johns are people. In our framework, classifiers that identify entities and relations among them are first learned from local information in the sentence; this information, along with constraints induced among entity types and relations, is used to perform global inference that accounts for the mutual dependencies among the entities. Our preliminary experimental results are promising and show that our global inference approach improves over learning relations and entities separately.