A unified model of phrasal and sentential evidence for Information Extraction

EMNLP 2009 - Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: A Meeting of SIGDAT, a Special Interest Group of ACL, Held in Conjunction with ACL-IJCNLP 2009

Volumn , Issue , 2009, Pages 151-160

  Patwardhan, Siddharth ^a   Riloff, Ellen ^a  

^a Department of Electrical and Computer Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FILLERS; INFORMATION RETRIEVAL;

EVENT EXTRACTION; INFORMATION EXTRACTION SYSTEMS; LOCAL CONTEXTS; PROBABILISTIC FRAMEWORK; UNIFIED MODELING;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 80053430767     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/1699510.1699530     Document Type: Conference Paper

References (21)

1. R. Bunescu and R. Mooney. 2004. Collective Information Extraction with Relational Markov Networks. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, pages 438-445, Barcelona, Spain, July.
2. M. Califf and R. Mooney. 2003. Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction. Journal of Machine Learning Research, 4:177-210, December.
3. H. Chieu, H. Ng, and Y. Lee. 2003. Closing the Gap: Learning-Based Information Extraction Rivaling Knowledge-Engineering Methods. In Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, pages 216-223, Sapporo, Japan, July.
4. P. Domingos and M. Pazzani. 1996. Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. In Proceedings of the Thirteenth International Conference on Machine Learning, pages 105-112, Bari, Italy, July.
5. J. Finkel, T. Grenager, and C. Manning. 2005. Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, pages 363-370, Ann Arbor, MI, June.
6. D. Freitag and A. McCallum. 2000. Information Extraction with HMM Structures Learned by Stochastic Optimization. In Proceedings of the Seventeenth National Conference on Artificial Intelligence, pages 584-589, Austin, TX, August.
7. D. Freitag. 1998. Toward General-Purpose Learning for Information Extraction. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, pages 404-408, Montreal, Quebec, August.
8. H. Ji and R. Grishman. 2008. Refining Event Extraction through Cross-Document Inference. In Proceedings of ACL-08: HLT, pages 254-262, Columbus, OH, June.
9. T. Joachims. 1998. Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In Proceedings of the Tenth European Conference on Machine Learning, pages 137-142, April.
10. C. Manning, P. Raghavan, and H Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press, New York, NY.
11. M. Maslennikov and T. Chua. 2007. A Multiresolution Framework for Information Extraction from Free Text. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 592-599, Prague, Czech Republic, June.
12. S. Patwardhan and E. Riloff. 2007. Effective Information Extraction with Semantic Affinity Patterns and Relevant Regions. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 717-727, Prague, Czech Republic, June.
13. W. Phillips and E. Riloff. 2007. Exploiting Role-Identifying Nouns and Expressions for Information Extraction. In Proceedings of International Conference on Recent Advances in Natural Language Processing, pages 165-172, Borovets, Bulgaria, September.
14. E. Riloff and W. Phillips. 2004. An Introduction to the Sundance and AutoSlog Systems. Technical Report UUCS-04-015, School of Computing, University of Utah.
15. E. Riloff. 1996. Automatically Generating Extraction Patterns from Untagged Text. In Proceedings of the Thirteenth National Conference on Articial Intelligence, pages 1044-1049, Portland, OR, August.
16. S. Soderland, D. Fisher, J. Aseltine, and W. Lehnert. 1995. CRYSTAL: Inducing a Conceptual Dictionary. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pages 1314-1319, Montreal, Canada, August.
17. B. Sundheim. 1992. Overview of the Fourth Message Understanding Evaluation and Conference. In Proceedings of the FourthMessage Understanding Conference (MUC-4), pages 3-21, McLean, VA, June.
18. V. Vapnik. 1995. The Nature of Statistical Learning Theory. Springer, New York, NY.
19. I. Witten and E. Frank. 2005. Data Mining-Practical Machine Learning Tools and Techniques. Morgan-Kaufmann, San Francisco, CA.
20. R. Yangarber, R. Grishman, P. Tapanainen, and S. Huttunen. 2000. Automatic Acquisition of Domain Knowledge for Information Extraction. In Proceedings of the 18th International Conference on Computational Linguistics, pages 940-946, Saarbr ücken, Germany, August.
21. B. Zadrozny and C. Elkan. 2001. Obtaining Calibrated Probability Estimates from Decision Trees and Naïve Bayesian Classiers. In Proceedings of the Eighteenth International Conference on Machine Learning, pages 609-616,Williamstown,MA, June.