Triangular-chain conditional random fields

IEEE Transactions on Audio, Speech and Language Processing

Volumn 16, Issue 7, 2008, Pages 1287-1302

  Jeong, Minwoo ^a   Lee, Gary Geunbae ^a  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

Author keywords

Conditional random fields (CRFs); Probabilistic sequence modeling; Spoken language understanding; Triangularchain structure

Indexed keywords

BASELINE MODELS; CONDITIONAL RANDOM FIELD; CONDITIONAL RANDOM FIELDS (CRFS); GRAPHICAL STRUCTURES; NAMED ENTITY RECOGNITION; NATURAL LANGUAGE PROCESSING; PARAMETER ESTIMATION METHOD; PROBABILISTIC MODELS; PROBABILISTIC SEQUENCE MODELING; REAL-WORLD; SCIENTIFIC FIELDS; SEQUENCE CLASSIFICATION; SEQUENTIAL DATA; SEQUENTIAL MODELING; SPOKEN LANGUAGE UNDERSTANDING; SYNTHETIC DATA; TRIANGULARCHAIN STRUCTURE;

COMPUTATIONAL LINGUISTICS; CONTENT BASED RETRIEVAL; LINGUISTICS; NATURAL LANGUAGE PROCESSING SYSTEMS; PARAMETER ESTIMATION; TEXT PROCESSING;

SPEECH RECOGNITION;

EID: 61749088295     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2008.925143     Document Type: Article

References (42)

1. T. G. Dietterich, "Machine learning for sequential data: A review, " in Structural, Syntactic, and Statistical Pattern Recognition, T. Caelli, Ed. : Springer, 2002, vol. 2396, Lecture Notes in Computer Science, pp. 15-30.
2. C. D. Manning and H. Schütze, Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press, 1999.
3. F. Sha and F. Pereira, "Shallow parsing with conditional random fields, " in Proc. Conf. North Amer. Chap. Assoc. Comput. Linguist. Human Lang. Technol. (NAACL/HLT), 2003, pp. 134-144.
4. C. Sutton and A. McCallum, "Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data, " J. Mach. Learn. Res., vol. 8, no. 1, pp. 693-723, Mar. 2007.
5. F. Sha and L. K. Saul, "Large margin hidden Markov models for automatic speech recognition, " in Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2007, pp. 1249-1256.
6. A. Stolcke, K. Ries, N. Coccaro, E. Shriberg, R. Bates, D. Jurafsky, P. Taylor, R. Martin, C. Van Ess-Dykema, and M. Meteer, "Dialogue act modeling for automatic tagging and recognition of conversational speech, " Comput. Linguist., vol. 26, no. 3, pp. 339-373, 2000.
7. S. Wang, A. Quattoni, L. P. Morency, D. Demirdjian, and T. Darrell, "Hidden conditional random fields for gesture recognition, " in Proc. IEEE Conf. Comput. Vision Pattern Recognition (CVPR), 2006, vol. 2, pp. 1521-1527.
8. A. Gunawardana, M. Mahajan, A. Acero, and J. C. Platt, "Hidden conditional random fields for phone classification, " in Proc. Interspeech 2005-Eurospeech, 2005, pp. 1117-1120.
9. D. A. Reynolds and R. C. Rose, "Robust text-independent speaker identification using Gaussian mixture speaker models, " IEEE Trans. Speech Audio Process., vol. 3, no. 1, pp. 72-83, Jan. 1995.
10. N. Gupta, G. Tur, D. Hakkani-Tur, S. Bangalore, G. Riccardi, and M. Gilbert, "The AT&T spoken language understanding system, " IEEE Trans. Audio, Speech, Lang. Process., vol. 14, no. 1, pp. 213-222, Jan. 2006.
11. S. Miller, H. Fox, L. Ramshaw, and R.Weischedel, "A novel use of statistical parsing to extract information from text, " in Proc. Conf. North Amer. Chap. Assoc. Comput. Linguist. (NACACL), 2000, pp. 226-233.
12. K. Toutanova, A. Haghighi, and C. D. Manning, "Joint learning improves semantic role labeling, " in Proc. Conf. Assoc. Comput. Linguist. (ACL), Ann Arbor, MI, Jun. 2005, pp. 589-596.
13. M. Mohri, F. Pereira, and M. Riley, "Weighted finite-state transducers in speech recognition, " Comput. Speech Lang., vol. 16, no. 1, pp. 69-88, 2002.
14. M. Jeong and G. G. Lee, "Jointly predicting dialog act and named entity for spoken language understanding, " in Proc. IEEE/ACL 2006 Workshop Spoken Lang. Technol., Dec. 2006, pp. 66-69.
15. J. Lafferty, A. McCallum, and F. Pereira, "Conditional random fields: Probabilistic models for segmenting and labeling sequence data, " in Proc. Int. Conf. Mach. Learn. (ICML), 2001, pp. 282-289.
16. C. Sutton and A. McCallum, "An introduction to conditional random fields for relational learning, " in Introduction to Statistical Relational Learning, L. Getoor and B. Taskar, Eds. Cambridge, MA:MIT Press, 2006.
17. L. E. Baume, T. Peterie, G. Souled, and N. Weiss, "A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains, " Ann. Math. Statist., vol. 41, no. 1, pp. 164-171, 1970.
18. A. J. Viterbi, "Error bounds for convolutional codes and asymptotically optimum decoding algorithm, " IEEE Trans. Inf. Theory, vol. IT-13, no. 2, pp. 260-267, Apr. 1967.
19. A. L. Berger, S. D. Pietra, and V. D. Pietra, "A maximum entropy approach to natural language processing, " Comput. Linguist., vol. 22, no. 1, pp. 39-71, 1996.
20. F. Peng and A. McCallum, "Accurate information extraction from research papers using conditional random fields, " in Proc. Conf. North Amer. Chap. Assoc. Comput. Linguist. Human Lang. Technol. (NAACL/HLT), 2004, pp. 329-336.
21. R. McDonald and F. Pereira, "Identifying gene and protein mentions in text using conditional random fields, " BMC Bioinformatics, vol. 6, no. Suppl 1, May 2005.
22. M. Jeong and G. G. Lee, "Practical use of non-local features for statistical spoken language understanding, " Comput. Speech Lang., vol. 22, no. 2, pp. 148-170, Apr. 2008.
23. A. Quattoni, S. Wang, L. P. Morency, M. Collins, and T. Darrell, "Hidden conditional random fields, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 10, pp. 1848-1852, Oct. 2007.
24. F. R. Kschischang, B. J. Frey, and H. A. Loeliger, "Factor graphs and the sum-product algorithm, " IEEE Trans. Inf. Theory, vol. 47, Feb. 2001.
25. T. Cohn, "Efficient inference in large conditional random fields, " in Proc. Eur. Conf. Mach. Learn. (ECML), 2006, pp. 606-613.
26. J. Nocedal and S. J. Wright, Numerical Optimization. New York: Springer, 1999.
27. J. Besag, "Statistical analysis of non-lattice data, " The Statistician, vol. 24, pp. 179-195, 1975.
28. H. Daumé III and D. Marcu, "Domain adaptation for statistical classifiers, " J. Artif. Intell. Res., vol. 26, pp. 101-126, 2006.
29. M. Jordan and R. Jacobs, "Hierarchical mixtures of experts and the EM algorithm, " Neural Comput., vol. 6, no. 1, pp. 181-214, 1994.
30. L.-P. Morency, C. Sidner, C. Lee, and T. Darrell, "The role of context in head gesture recognition, " in Proc. 21st National Conf. Artif. Intell. (AAAI), 2006, pp. 1650-1653.
31. R. McDonald, K. Hannan, T. Neylon, M. Wells, and J. Reynar, "Structured models for fine-to-coarse sentiment analysis, " in Proc. Conf. Assoc. Comput. Linguist. (ACL), Prague, Czech Republic, Jun. 2007, pp. 432-439.
32. Y.Wang, L. Deng, and A. Acero, "Spoken language understanding: An introduction to the statistical framework, " IEEE Signal Process. Mag., vol. 22, no. 5, pp. 16-31, Sep. 2005.
33. Y. He and S.Young, "Semantic processing using the hidden vector state model, " Comput. Speech Lang., vol. 19, no. 1, pp. 85-106, Jan. 2005.
34. L. A. Ramshaw and M. P. Marcus, "Text chunking using transformation- based learning, " in Proc. 3rd Workshop Very Large Corpora., 1995, pp. 82-94.
35. L. Gillick and S. Cox, "Some statistical issues in the comparison of speech recognition algorithms, " in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), 1989, pp. 532-535.
36. S. Young, G. Evermann, M. Gales, T. Hain, D. Kershaw, G. Moore, J. D. O. Odell, D. Povey, V. Valtchev, and P. Woodland, "The HTK Book: Version 3.3, " Tech. Rep. Cambridge Univ., U.K., 2005.
37. G. E. Hinton, J. L. McClelland, and D. E. Rumelhart, "Distributed representations, " in Parallel Distributed Processing, L. Getoor and B. Taskar, Eds. Cambridge, MA: MIT Press, 1986, vol. 1, ch. 3, pp. 77-109.
38. L. R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition, " Proc. IEEE, vol. 77, no. 2, pp. 257-286, Feb. 1989.
39. B. Taskar, C. Guestrin, and D.Koller, "Max-margin Markov networks, " Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2003.
40. C. Pal, C. Sutton, and A. McCallum, "Sparse forward-backward using minimum divergence beams for fast training of conditional random fields, " in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), 2006, pp. V-581-V-584.
41. C. Sutton and A. McCallum, "Piecewise pseudolikelihood for efficient CRF training, " in Proc. Int. Conf. Mach. Learn. (ICML), 2007, pp. 863-870.
42. S. Vishwanathan, N. N. Schraudolph, M. W. Schmidt, and K. Murphy, "Accelerated training of conditional random fields with stochastic meta-descent, " in Proc. Int. Conf. Mach. Learn. (ICML), 2006, pp. 969-976.