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Combining PCFG-LA models with dual decomposition: a case study with function labels and binarization

Le Roux, Joseph, Rozenknop, Antoine and Foster, Jennifer orcid logoORCID: 0000-0002-7789-4853 (2013) Combining PCFG-LA models with dual decomposition: a case study with function labels and binarization. In: International Conference on Empirical Methods in Natural Language Processing, 18-21 Oct 2013, Seattle, WA..

It has recently been shown that different NLP models can be effectively combined using dual decomposition. In this paper we demonstrate that PCFG-LA parsing models are suit- able for combination in this way. We experiment with the different models which result from alternative methods of extracting a gram- mar from a treebank (retaining or discarding function labels, left binarization versus right binarization) and achieve a labeled Parseval F-score of 92.4 on Wall Street Journal Section 23 – this represents an absolute improvement of 0.7 and an error reduction rate of 7% over a strong PCFG-LA product-model base- line. Although we experiment only with binarization and function labels in this study, there is much scope for applying this approach to other grammar extraction strategies.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:Dual decomposition; Parsing models
Subjects:Computer Science > Computational linguistics
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. . Association for Computational Linguistics.
Publisher:Association for Computational Linguistics
Copyright Information:© 2013 ACL
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:19959
Deposited On:26 May 2014 12:54 by Jennifer Foster . Last Modified 10 Oct 2018 13:49

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