- (Roth & Yih, 2002) ⇒ Dan Roth, Wen-tau Yih. (2002). “Probabilistic Reasoning for Entity & Relation Recognition.” In: Proceedings of the 20th International Conference on Computational Linguistics (COLING 2002).
- It proposes an algorithm for recognizing relations and entities in sentences that takes mutual dependencies among them into account.
- (Ji & Grishman, 2006) ⇒ Heng Ji, and Ralph Grishman. (2006). “Analysis and Repair of Name Tagger Errors.” In: Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics (ACL 2006).
- QUOTE: (Roth and Yi 2002, 2004), given name boundaries in the text, separate classifiers are first trained for name classification and semantic relation detection. Then, the output of the classifiers is used as a conditional distribution given the observed data. This information, along with the constraints among the relations and entities (specific relations require specific classes of names).
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.
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|Author||Dan Roth + and Wen-tau Yih +|
|journal||Proceedings of the 20th International Conference on Computational Linguistics +|
|title||Probabilistic Reasoning for Entity & Relation Recognition +|