Difference between revisions of "Structured Linear Classifier"
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| − | We describe new algorithms for training tagging models, as an alternative to maximum-entropy models or conditional random | + | 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. |
Latest revision as of 05:08, 6 November 2012
Online
Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithms, Collins. 2002, InProc. EMNLP. Weblink
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.
