2002 ProbReasoningForEntityAndRelRecognition
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- (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).
Subject Headings: Entity Mention Recognition, Relation Mention Recognition, Joint Resolution.
Notes
- It proposes an algorithm for recognizing relations and entities in sentences that takes mutual dependencies among them into account.
Cited By
2006
- (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).
Quotes
Abstract
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.
References
- Steven P. Abney. (1991). Parsing by chunks. In Steven P. Abney R. C. Berwick and C. Tenny, editors, Principle-based parsing: Computation and Psycholinguistics, pages 257–278. Kluwer, Dordrecht.
- Christopher M. Bishop, (1995). Neural Networks for Pattern Recognition, chapter 6.4: Modelling conditional distributions, page 215. Oxford University Press.
- M. Califf and Raymond Mooney. (1999). Relational learning of pattern-match rules for information extraction. In National Conference on Artificial Intelligence.
- A. Carleson, C. Cumby, J. Rosen, and Dan Roth. (1999). The SNoW learning architecture. Technical Report UIUCDCS-R-99-2101, UIUC Computer Science Department, May.
- Michael Collins and Yoram Singer. (1999). Unsupervised models for name entity classification. In EMNLP-VLC’99, the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, June.
- Dayne Freitag. (2000). Machine learning for information extraction in informal domains. Machine Learning, 39(2/3):169–202.
- R. Gallager. 1962. Low density parity check codes. IRE Trans. Info. Theory, IT-8:21–28, Jan.
- Lynette Hirschman, M. Light, E. Breck, and J. Burger. (1999). Deep read: A reading comprehension system. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics.
- John D. Lafferty, Andrew McCallum, and Fernando Pereira. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the International Conference on Machine Learning.
- N. Littlestone. (1988). Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning, 2:285–318.
- D. MacKay. (1999). Good error-correcting codes based on very sparse matrices. IEEE Transactions on Information Theory, 45.
- M. Munoz, V. Punyakanok, Dan Roth, and D. Zimak. (1999). A learning approach to shallow parsing. In EMNLP-VLC’99, the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, June.
- K. Murphy, Y.Weiss, and Michael I. Jordan. (1999). Loopy belief propagation for approximate inference: An empirical study. In: Proceedings of Uncertainty in AI, pages 467–475. J. Pearl. (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann.
- V. Punyakanok and Dan Roth. (2001). The use of classifiers in sequential inference. In NIPS-13; The 2000 Conference on Advances in Neural Information Processing Systems.
- Dan Roth and W. Yih. (2001). Relational learning via propositional algorithms: An information extraction case study. In: Proceedings of the International Joint Conference on Artificial Intelligence, pages 1257–1263.
- Dan Roth. (1996). On the hardness of approximate reasoning. Artificial Inteligence, 82(1-2):273–302, April.
- Dan Roth. (1998). Learning to resolve natural language ambiguities: A unified approach. In: Proceedings of National Conference on Artificial Intelligence, pages 806–813.
- E. Voorhees. (2000). Overview of the trec-9 question answering track. In The Ninth Text Retrieval Conference (TREC-9), pages 71–80. NIST SP 500-249.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2002 ProbReasoningForEntityAndRelRecognition | Wen-tau Yih | Probabilistic Reasoning for Entity & Relation Recognition | http://acl.ldc.upenn.edu/coling2002/proceedings/data/area-19/co-386.pdf |