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Discriminative Training of a Neural Network Statistical Parser

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Published in ACL '04: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics. Barcelona (Spain) - 21-26 July 2004 - East Stroudsburg, PA: Association for Computational Linguistics. 2004, p. 95-102
Abstract Discriminative methods have shown significant improvements over traditional generative methods in many machine learning applications, but there has been difficulty in extending them to natural language parsing. One problem is that much of the work on discriminative methods conflates changes to the learning method with changes to the parameterization of the problem. We show how a parser can be trained with a discriminative learning method while still parameterizing the problem according to a generative probability model. We present three methods for training a neural network to estimate the probabilities for a statistical parser, one generative, one discriminative, and one where the probability model is generative but the training criteria is discriminative. The latter model outperforms the previous two, achieving state-of-the-art levels of performance (90.1% F-measure on constituents).
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ISBN: 1932432329
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Research groups Geneva Artificial Intelligence Laboratory
Laboratoire d'Analyse et de Traitement du Langage (LATL)
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(ISO format)
HENDERSON, James. Discriminative Training of a Neural Network Statistical Parser. In: ACL '04: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics. Barcelona (Spain). East Stroudsburg, PA : Association for Computational Linguistics, 2004. p. 95-102. doi: 10.3115/1218955.1218968 https://archive-ouverte.unige.ch/unige:120653

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Deposited on : 2019-07-12

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