Learning in natural language: Theory and algorithmic approaches

Proceedings of the 4th Conference on Computational Natural Language Learning, CoNLL 2000 and of the 2nd Learning Language in Logic Workshop, LLL 2000 - Held in cooperation with ICGI 2000

Volumn , Issue , 2000, Pages 1-6

  Roth, Dan ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMIC LANGUAGES; LEARNING ALGORITHMS; NATURAL LANGUAGE PROCESSING SYSTEMS;

ALGORITHMIC APPROACH; DISTRIBUTION-FREE; KNOWLEDGE-REPRESENTATION; LANGUAGE THEORY; LEARNING THEORY; LINEAR REPRESENTATION; MODELS OF LEARNING; NATURAL LANGUAGES; SIMPLE++; STATISTIC-BASED;

KNOWLEDGE REPRESENTATION;

EID: 85117736878     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (44)

1. J. A. Aslam and S. E. Decatur. Specification and simulation of statistical query algorithms for efficiency and noise tolerance. In COLT 1995, pages 437-446.
2. E. Brill and P. Resnik. A rule-based approach to prepositional phrase attachment disambiguation. In Proc. of COLING, 1994.
3. E. Brill. Transformation-based error-driven learning and natural language processing: A case study in part of speech tagging. Computational Linguistics, 21(4):543-565, 1995.
4. M. Collins and J Brooks. Prepositional phrase attachment through a backed-off model. In WVLC 1995.
5. A. Carleson, C. Cumby, J. Rosen, and D. Roth. The SNoW learning architecture. Technical Report UIUCDCS-R-99-2101, UIUC CS, May 1999.
6. W. Cohen. PAC-learning recursive logic programs: Efficient algorithms. JAIR, 2:501-539, 1995.
7. C. Cumby and D. Roth. Relational representations that facilitate learning. In KR 2000, pages 425-434.
8. M. Collins and Y. Singer. Unsupervised models for name entity classification. In EMNLP-VLC'99.
9. W. Daelemans, A. van den Bosch, and J. Zavrel. Forgetting exceptions is harmful in language learning. Machine Learning, 34(1-3): 11-43, 1999.
10. S. E. Decatur. Statistical queries and faulty PAC oracles. In COLT 1993, pages 262-268.
11. R. O. Duda and P. E. Hart. Pattern Classification and Scent Analysis. Wiley, 1973.
12. J. N. Darroch and D. Ratcliff. Generalized iterative scaling for log-linear models. Annals of Mathematical Statistics, 43(5):1470-1480, 1972.
13. Y. Freund and R. Schapire. Large margin classification using the Perceptron algorithm. In COLT 1998.
14. W. Gale, K. Church, and D. Yarowsky. A method for disambiguating word senses in a large corpus. Computers and the Humanities, 26:415-439, 1993.
15. A. R. Golding. A Bayesian hybrid method for context-sensitive spelling correction. In Proceedings of the 3rd workshop on very large corpora, ACL-95, 1995.
16. A. R. Golding and D. Roth. A Winnow based approach to context-sensitive spelling correction. Machine Learning, 34(1-3):107-130, 1999.
17. A. Grove and D. Roth. Linear concepts and hidden variables. Machine Learning, 2000. To Appear.
18. D. Haussler. Decision theoretic generalizations of the PAC model for neural net and other learning applications. Inform. Comput., 100(1):78-150, 1992.
19. E. T. Jaynes. On the rationale of maximum-entropy methods. Proc. of the IEEE, 70(9):939-952, 1982.
20. S. M. Katz. Estimation of probabilities from sparse data for the language model component of a speech recognizer. IEEE Transactions on Acoustics, speech, and Signal Processing, 35(3):400-401, 1987.
21. M. Kearns. Efficient noise-tolerant learning from statistical queries. In COLT 1993, pages 392-401.
22. R. Khardon and D. Roth. Learning to reason. Journal of the ACM, 44(5):697-725, Sept. 1997.
23. R. Khardon, D. Roth, and L. G. Valiant. Relational learning for NLP using linear threshold elements. In IJCAI 1999, pages 911-917.
24. M. J. Kearns, R. E. Schapire, and L. M. Sellie. Toward efficient agnostic learning. Machine Learning, 17(2/3):115-142, 1994.
25. J. Kupiec. Robust part-of-speech tagging using a hidden Markov model. Computer Speech and Language, 6:225-242, 1992.
26. J. Kivinen and M. K. Warmuth. Exponentiated gradient versus gradient descent for linear predictors. In STOC, 1995.
27. N. Littlestone. Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning, 2:285-318, 1988.
28. L. Mangu and E. Brill. Automatic rule acquisition for spelling correction. In ICML 1997, pages 734-741.
29. S. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 20:629-679, 1994.
30. M. Munoz, V. Punyakanok, D. Roth, and D. Zimak. A learning approach to shallow parsing. In EMNLPVLC'99, pages 168-178.
31. V. Punyakanok and D. Roth. Inference with classifiers. Technical Report UIUCDCS-R-2000-2181, UIUC CS.
32. L. R. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257-285, 1989.
33. A. Ratnaparkhi. A linear observed time statistical parser based on maximum entropy models. In EMNLP-97.
34. F. Rosenblatt. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65:386-407, 1958.
35. D. Roth. Learning to resolve natural language ambiguities: A unified approach. In AAAI'98, pages 806-813.
36. D. Roth. Learning in natural language. In IJCAI 1999, pages 898-904.
37. D. Roth. Memory based learning in NLP. Technical Report UIUCDCS-R-99-2125, UIUC CS, March 1999.
38. D. Roth. Learning in natural language. Technical Report UIUCDCS-R-2000-2180, UIUC CS July 2000.
39. H. Schütze. Distributional part-of-speech tagging. In EACL 1995.
40. L. G. Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134-1142, November 1984.
41. L. G. Valiant. Robust logic. In STOC 1999.
42. V. N. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, New York, 1995.
43. D. Yarowsky. Decision lists for lexical ambiguity resolution: application to accent restoration in Spanish and French. In ACL 1994, pages 88-95.
44. J. Zavrel and W. Daelemans. Memory-based learning: Using similarity for smoothing. In ACL, 1997.