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Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms (2002) [194 citations — 9 self]

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@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.

Citations

1196	Building a large annotated corpus of English: the penn treebank – Marcus, Marcinkiewicz, et al. - 1993
848	Conditional random fields: Probabilistic models for segmenting and labeling sequence data – Lafferty, McCallum, et al. - 2001
566	Transformation-based error-driven learning and natural language processing: A case study in part-of-speech tagging – Brill - 1995
267	Text chunking using transformation-based learning – Ramshaw, Marcus - 1995
259	Maximum entropy markov models for information extraction and segmentation – McCallum, Freitag, et al. - 2000
239	A maximum entropy part-of-speech tagger – Ratnaparkhi - 1996
204	Large margin classification using the perceptron algorithm – Freund, Schapire - 1999
131	Convolution kernels for natural language – Collins, Duffy - 2001
108	New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron – Collins, Duffy - 2002
44	On weak learning – Helmbold, Warmuth - 1995
14	Conditional random Probabilistic models for segmenting and labeling sequence data – Laerty, McCallum, et al. - 2001
9	Ranking Algorithms for Named Entity Extraction: Boosting and the Voted Perceptron – Collins
2	Large margin classication using the Perceptron algorithm – Freund, Schapire - 1999