Automatic event extraction with structured preference modeling

50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference

Volumn 1, Issue , 2012, Pages 835-844

  Lu, Wei ^a   Roth, Dan ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONDITIONAL RANDOM FIELD; EVENT EXTRACTION; LEARNING FRAMEWORKS; LEARNING PROCEDURES; MODEL-BASED OPC; PREFERENCE MODELING; SEQUENCE LABELING; TYPE INFORMATION;

COMPUTATIONAL LINGUISTICS;

DATA MINING;

EID: 84878184708     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. T. Berg-Kirkpatrick, A. Bouchard-Côté, J. DeNero, and D. Klein. 2010. Painless unsupervised learning with features. In Proc. of HLT-NAACL'10, pages 582-590.
2. J. Besag. 1975. Statistical analysis of non-lattice data. The Statistician, pages 179-195.
3. M. Chang, L. Ratinov, and D. Roth. 2007. Guiding semisupervision with constraint-driven learning. In Proc. of ACL'07, pages 280-287.
4. M. Chang, D. Goldwasser, D. Roth, and V. Srikumar. 2010a. Discriminative learning over constrained latent representations. In Proc. of NAACL'10, 6.
5. M. Chang, V. Srikumar, D. Goldwasser, and D. Roth. 2010b. Structured output learning with indirect supervision. In Proc. ICML'10.
6. K. Ganchev, J. Graca, J. Gillenwater, and B. Taskar. 2010. Posterior regularization for structured latent variable models. The Journal of Machine Learning Research (JMLR), 11:2001-2049.
7. A. Haghighi and D. Klein. 2006. Prototype-driven learning for sequence models. In Proc. of HLT-NAACL'06, pages 320-327.
8. J. D. Lafferty, A. McCallum, and F. C. N. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proc. of ICML'01, pages 282-289.
9. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proc. of the IEEE, pages 2278-2324.
10. D.C. Liu and J. Nocedal. 1989. On the limited memory bfgs method for large scale optimization. Mathematical programming, 45(1):503-528.
11. D. Okanohara, Y. Miyao, Y. Tsuruoka, and J. Tsujii. 2006. Improving the scalability of semi-markov conditional random fields for named entity recognition. In Proc. of ACL'06, pages 465-472.
12. H. Poon, C. Cherry, and K. Toutanova. 2009. Unsupervised morphological segmentation with log-linear models. In Proc. of HLT-NAACL'09, pages 209-217.
13. L. Ratinov and D. Roth. 2009. Design challenges and misconceptions in named entity recognition. In Proc. of CoNLL'09, pages 147-155.
14. L. Ratinov, D. Roth, D. Downey, and M. Anderson. 2011. Local and global algorithms for disambiguation to wikipedia. In Proc. of ACL-HLT'11, pages 1375- 1384.
15. D. Roth and W. Yih. 2005. Integer linear programming inference for conditional random fields. In Proc. of ICML'05, pages 736-743.
16. R. Samdani, M. Chang, and D. Roth. 2012. Unified expectation maximization. In Proc. NAACL'12.
17. S. Sarawagi and W.W. Cohen. 2004. Semi-markov conditional random fields for information extraction. NIPS'04, pages 1185-1192.
18. N.A. Smith and J. Eisner. 2005a. Contrastive estimation: Training log-linear models on unlabeled data. In Proc. of ACL'05, pages 354-362.
19. N.A. Smith and J. Eisner. 2005b. Guiding unsupervised grammar induction using contrastive estimation. In Proc. of IJCAI Workshop on Grammatical Inference Applications, pages 73-82.
20. J. Strötgen and M. Gertz. 2010. Heideltime: High quality rule-based extraction and normalization of temporal expressions. In Proc. of SemEval'10, pages 321- 324.