Self-Training PCFG Grammars with Latent Annotations Across Languages

We investigate the effectiveness of selftraining 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 selftraining. 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 stateof-the-art parsing accuracies for a single

• Type: Journal
• Year: 2009
• Where: EMNLP
• Authors: Zhongqiang Huang, Mary P. Harper
• Keywords: EMNLP 2009, Natural Language Processing, Parser, Parsing, PCFG-LA Parser