Incorporating non-local information into information extraction systems by Gibbs sampling

ACL-05 - 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

Volumn , Issue , 2005, Pages 363-370

  Finkel, Jenny Rose ^a   Grenager, Trond ^a   Manning, Christopher ^a  

^a Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; DYNAMIC PROGRAMMING; INFORMATION RETRIEVAL; NATURAL LANGUAGE PROCESSING SYSTEMS; SIMULATED ANNEALING;

APPROXIMATE INFERENCE; CONSISTENCY CONSTRAINTS; INFORMATION EXTRACTION SYSTEMS; LONG-DISTANCE DEPENDENCIES; PROBABILISTIC MODELS; STATE-OF-THE-ART SYSTEM; STATISTICAL NATURAL LANGUAGE PROCESSING; VITERBI DECODING;

MONTE CARLO METHODS;

EID: 84859918687     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/1219840.1219885     Document Type: Conference Paper

References (22)

1. S. Abney. 1997. Stochastic attribute-value grammars. Computational Linguistics, 23:597-618. (Pubitemid 127458666)
2. C. Andrieu, N. de Freitas, A. Doucet, and M. I. Jordan. 2003. An introduction to MCMC for machine learning. Machine Learning, 50:5-43.
3. A. Borthwick. 1999. A Maximum Entropy Approach to Named Entity Recognition. Ph.D. thesis, New York University.
4. R. Bunescu and R. J. Mooney. 2004. Collective information extraction with relational Markov networks. In Proceedings of the 42nd ACL, pages 439-446.
5. H. L. Chieu and H. T. Ng. 2002. Named entity recognition: a maximum entropy approach using global information. In Proceedings of the 19th Coling, pages 190-196.
6. R. G. Cowell, A. Philip Dawid, S. L. Lauritzen, and D. J. Spiegelhalter. 1999. Probabilistic Networks and Expert Systems. Springer-Verlag, New York.
7. J. R. Curran and S. Clark. 2003. Language independent NER using a maximum entropy tagger. In Proceedings of the 7th CoNLL, pages 164-167.
8. S. Della Pietra, V. Della Pietra, and J. Lafferty. 1997. Inducing features of random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19:380-393. (Pubitemid 127762893)
9. J. Finkel, S. Dingare, H. Nguyen, M. Nissim, and C. D. Manning. 2004. Exploiting context for biomedical entity recognition: from syntax to the web. In JointWorkshop on Natural Language Processing in Biomedicine and Its Applications at Coling 2004.
10. D. Freitag and A. McCallum. 1999. Information extraction with HMMs and shrinkage. In Proceedings of the AAAI-99 Workshop on Machine Learning for Information Extraction.
11. D. Freitag. 1998. Machine learning for information extraction in informal domains. Ph.D. thesis, Carnegie Mellon University.
12. S. Geman and D. Geman. 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transitions on Pattern Analysis and Machine Intelligence, 6:721-741. (Pubitemid 15453722)
13. M. Kim, Y. S. Han, and K. Choi. 1995. Collocation map for overcoming data sparseness. In Proceedings of the 7th EACL, pages 53-59.
14. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. 1983. Optimization by simulated annealing. Science, 220:671-680.
15. P. J. Van Laarhoven and E. H. L. Arts. 1987. Simulated Annealing: Theory and Applications. Reidel Publishers.
16. J. Lafferty, A. McCallum, and F. Pereira. 2001. Conditional Random Fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the 18th ICML, pages 282-289. Morgan Kaufmann, San Francisco, CA.
17. T. R. Leek. 1997. Information extraction using hidden Markov models. Master's thesis, U.C. San Diego.
18. R. Malouf. 2002. Markov models for language-independent named entity recognition. In Proceedings of the 6th CoNLL, pages 187-190.
19. A. Mikheev, M. Moens, and C. Grover. 1999. Named entity recognition without gazetteers. In Proceedings of the 9th EACL, pages 1-8.
20. L. R. Rabiner. 1989. A tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257-286.
21. C. Sutton and A. McCallum. 2004. Collective segmentation and labeling of distant entities in information extraction. In ICML Workshop on Statistical Relational Learning and Its connections to Other Fields.
22. B. Taskar, P. Abbeel, and D. Koller. 2002. Discriminative probabilistic models for relational data. In Proceedings of the 18th Conference on Uncertianty in Artificial Intelligence (UAI-02), pages 485-494, Edmonton, Canada.