This paper describes an incremental parsing approach where parameters are estimated using a variant of the perceptron algorithm. A beam-search algorithm is used during both training and decoding phases of the method. The perceptron approach was implemented with the same feature set as that of an existing generative model (Roark, 2001a), and experimental results show that it gives competitive performance to the generative model on parsing the Penn treebank. We demonstrate that training a perceptron model to combine with the generative model during search provides a 2.1 percent F-measure improvement over the generative model alone, to 88.8 percent.
Incremental Parsing with the Perceptron Algorithm
Published 2004 in Annual Meeting of the Association for Computational Linguistics
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- Publication year
2004
- Venue
Annual Meeting of the Association for Computational Linguistics
- Publication date
2004-07-21
- Fields of study
Computer Science
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