%0 Conference Paper %1 collins2002perceptron %A Collins, Michael %B Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10 %C Stroudsburg, PA, USA %D 2002 %I Association for Computational Linguistics %K averaged docrped perceptron prediction sequential structured viterbi %P 1--8 %R 10.3115/1118693.1118694 %T Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms %U http://dx.doi.org/10.3115/1118693.1118694 %X 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.

@inproceedings{collins2002perceptron, 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.}, acmid = {1118694}, added-at = {2012-10-25T19:28:25.000+0200}, address = {Stroudsburg, PA, USA}, author = {Collins, Michael}, biburl = {https://www.bibsonomy.org/bibtex/2710aacdc4a3b3281cf8cb6cf8beaa231/jil}, booktitle = {Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10}, description = {Discriminative training methods for hidden Markov models}, doi = {10.3115/1118693.1118694}, interhash = {3612e78d171711e74bc0d1ed72ebee14}, intrahash = {710aacdc4a3b3281cf8cb6cf8beaa231}, keywords = {averaged docrped perceptron prediction sequential structured viterbi}, numpages = {8}, pages = {1--8}, publisher = {Association for Computational Linguistics}, series = {EMNLP '02}, timestamp = {2015-01-31T19:32:36.000+0100}, title = {Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms}, url = {http://dx.doi.org/10.3115/1118693.1118694}, year = 2002 }