Learning scripts as hidden markov models

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2014, Pages 1565-1571

  Orr, J Walker ^a   Tadepalli, Prasad ^a   Doppa, Janardhan Rao ^a   Fern, Xiaoli ^a   Dietterich, Thomas G ^a  

^a OREGON STATE UNIVERSITY   (United States)

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EID: 84908162531     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Bahl, L. R.; Jelinek, F.; and Mercer, R. L. 1983. A maximum likelihood approach to continuos speech recognition. IEEE Transactions in Pattern Analysis and Machine Intelligence (PAMI) 5(2): 179-190.
2. Baum, L. E.; Petrie, T.; Soules, G.; and Weiss, N. 1970. A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains. The Annals of Mathematical Statistics 41(1): 164-171.
3. Chambers, N., and Jurafsky, D. 2008. Unsupervised learning of narrative event chains. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics (ACL), 789-797.
4. Chambers, N. 2013. Event schema induction with a probabilistic entity-driven model. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 1797-1807.
5. DeJong, G., and Mooney, R. 1986. Explanation-based learning: An alternative view. Machine learning 1(2): 145-176.
6. DeJong, G. 1981. Generalizations based on explanations. In Proceedings of the Seventh International Joint Conference on Artificial Intelligence (IJCAI), 67-69.
7. Dupont, P.; Miclet, L.; and Vidal, E. 1994. What is the search space of the regular inference? In Grammatical Inference and Applications. Springer. 25-37.
8. Friedman, N. 1998. The Bayesian structural EM algorithm. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, 129-138. Morgan Kaufmann Publishers Inc.
9. Gupta, R., and Kochenderfer, M. J. 2004. Common sense data acquisition for indoor mobile robots. In AAAI, 605-610.
10. Kit Cheung, J. C.; Poon, H.; and Vanderwende, L. 2013. Probabilistic frame induction. In Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL.HLT), 837-846.
11. Krogh, A.; Brown, M.; Mian, I. S.; Sjolander, K.; and Haussler, D. 1994. Hidden markov models in computational biology. Journal of Molecular Biology 1501-1531.
12. Miller, G. A. 1995. Word Net: A lexical database for English. Communications of the ACM 38(11 ):39-41.
13. Rabiner, L. R. 1990. A tutorial on hidden Markov models and selected applications in speech recognition. 267-296.
14. Raghavan, P.; Fosler-Lussier, E.; and Lai, A. M. 2012. Learning to temporally order medical events in clinical text. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics (ACL), 70-74.
15. Regneri, M.; Roller, A.; and Pinkal, M. 2010. Learning script knowledge with web experiments. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, 979-988. Association for Computational Linguistics.
16. Schank, R., and Abelson, R. 1977. Scripts, plans, goals and understanding: An inquiry into human knowledge structures. Lawrence Erlbaum Publishers.
17. Seymore, K.; McCallum, A.; and Rosenfeld, R. 1999. Learning hidden Markov model structure for information extraction. In AAAI Workshop on Machine Learning for Information Extraction, 37-42.
18. Stolcke, A., and Omohundro, S. M. 1994. Best-first model merging for hidden Markov model induction. arXiv preprint cmp-lg/9405017.