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Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms

EMNLP, pp.1-8, (2002)

Cited by: 2271|Views222
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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...More

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