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Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms (2002)
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| Citations: | 660 - 13 self |
BibTeX
@INPROCEEDINGS{Collins02discriminativetraining,
author = {Michael Collins},
title = {Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms},
booktitle = {},
year = {2002},
pages = {1--8}
}
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Abstract
We describe new algorithms for training tagging models, as an alternative to maximum-entropy models or conditional random fields (CRFs). The algorithms rely on Viterbi decoding of training examples, combined with simple additive updates. We describe theory justifying the algorithms through a modification of the proof of convergence of the perceptron algorithm for classification problems. We give experimental results on part-of-speech tagging and base noun phrase chunking, in both cases showing improvements over results for a maximum-entropy tagger.
Keyphrases
perceptron algorithm discriminative training method hidden markov model base noun phrase chunking part-of-speech tagging classification problem conditional random field maximum-entropy model viterbi decoding new algorithm simple additive update experimental result training example maximum-entropy tagger
