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Neural Network Probability Estimation for Broad Coverage Parsing

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Published in EACL '03: proceedings of the Tenth Conference of European Chapter of the Association for Computational Linguistics. Volume 1. Budapest (Hungary) - 12-17 April 2003 - East Stroudsburg, PA: Association for Computational Linguistics. 2003, p. 131-138
Abstract We present a neural-network-based statistical parser, trained and tested on the Penn Treebank. The neural network is used to estimate the parameters of a generative model of left-corner parsing, and these parameters are used to search for the most probable parse. The parser's performance (88.8% F-measure) is within 1% of the best current parsers for this task, despite using a small vocabulary size (512 inputs). Crucial to this success is the neural network architecture's ability to induce a finite representation of the unbounded parse history, and the biasing of this induction in a linguistically appropriate way.
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ISBN: 1932432000
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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. Neural Network Probability Estimation for Broad Coverage Parsing. In: EACL '03: proceedings of the Tenth Conference of European Chapter of the Association for Computational Linguistics. Volume 1. Budapest (Hungary). East Stroudsburg, PA : Association for Computational Linguistics, 2003. p. 131-138. https://archive-ouverte.unige.ch/unige:120651

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Deposited on : 2019-07-12

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