Abstract
We investigate the effectiveness of self-training PCFG grammars with latent annotations (PCFG-LA) for parsing languages with different amounts of labeled training data. Compared to Charniak's lexicalized parser, the PCFG-LA parser was more effectively adapted to a language for which parsing has been less well developed (i.e., Chinese) and benefited more from self-training. We show for the first time that self-training is able to significantly improve the performance of the PCFG-LA parser, a single generative parser, on both small and large amounts of labeled training data. Our approach achieves state-of-the-art parsing accuracies for a single parser on both English (91.5%) and Chinese (85.2%).
Keywords
Computer scienceRule-based machine translationNatural language processingArtificial intelligenceProgramming language
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Publication Info
- Year
- 2009
- Type
- article
- Volume
- 2
- Pages
- 832-832
- Citations
- 87
- Access
- Closed
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Cite This
Zhongqiang Huang,
Mary P. Harper
(2009).
Self-training PCFG grammars with latent annotations across languages.
, 2
, 832-832.
https://doi.org/10.3115/1699571.1699621
Identifiers
- DOI
- 10.3115/1699571.1699621