Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms
作者： Michael Collins
关键词: Hidden Markov model 、 Artificial intelligence 、 Viterbi decoder 、 Maximum-entropy Markov model 、 Chunking (psychology) 、 Computer science 、 Machine learning 、 Viterbi algorithm 、 Discriminative model 、 Noun phrase 、 Iterative Viterbi decoding 、 Conditional random field 、 Pattern recognition 、 Perceptron 、 Algorithm 、 Structured prediction
摘要: We describe new algorithms for training tagging models, as an alternative to maximum-entropy models or conditional random fields (CRFs). The rely on Viterbi decoding of examples, combined with simple additive updates. theory justifying the through a modification proof convergence perceptron algorithm classification problems. give experimental results part-of-speech and base noun phrase chunking, in both cases showing improvements over tagger.
aclweb.org
acm.org
columbia.edu 参考文章(12)
L. A. Ramshaw, M. P. Marcus, Text Chunking Using Transformation-Based Learning meeting of the association for computational linguistics. pp. 157- 176 ,(1999) , 10.1007/978-94-017-2390-9_10
Mitch Marcus, Beatrice Santorini, Mary Ann Marcinkiewicz, None, Building a large annotated corpus of English: the penn treebank Computational Linguistics. ,vol. 19, pp. 313- 330 ,(1993) , 10.21236/ADA273556
Adwait Ratnaparkhi, A Maximum Entropy Model for Part-Of-Speech Tagging empirical methods in natural language processing. ,(1996)
Andrew McCallum, Dayne Freitag, Fernando C. N. Pereira, Maximum Entropy Markov Models for Information Extraction and Segmentation international conference on machine learning. pp. 591- 598 ,(2000)
D.P. Helmbold, M.K. Warmuth, On Weak Learning Journal of Computer and System Sciences. ,vol. 50, pp. 551- 573 ,(1995) , 10.1006/JCSS.1995.1044
Yoav Freund, Robert E. Schapire, Large margin classification using the perceptron algorithm conference on learning theory. ,vol. 37, pp. 209- 217 ,(1998) , 10.1145/279943.279985
F. Rosenblatt, The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review. ,vol. 65, pp. 386- 408 ,(1958) , 10.1037/H0042519
Eric Brill, Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging Computational Linguistics. ,vol. 21, pp. 543- 565 ,(1995)
Nigel Duffy, Michael Collins, Convolution Kernels for Natural Language neural information processing systems. ,vol. 14, pp. 625- 632 ,(2001)
Michael Collins, Nigel Duffy, New ranking algorithms for parsing and tagging Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02. pp. 263- 270 ,(2001) , 10.3115/1073083.1073128