Statistical parsing with probabilistic symbol-refined tree substitution grammars

Authors:
Hiroyuki Shindo;Yusuke Miyao;Akinori Fujino;Masaaki Nagata
Affiliations:
NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan;National Institute of Informatics, Tokyo, Japan;NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan;NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

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Abstract

We present probabilistic Symbol-Refined Tree Substitution Grammars (SR-TSG) for statistical parsing of natural language sentences. An SR-TSG is an extension of the conventional TSG model where each nonterminal symbol can be refined (subcategorized) to fit the training data. Our probabilistic model is consistent based on the hierarchical Pitman-Yor Process to encode backoff smoothing from a fine-grained SR-TSG to simpler CFG rules, thus all grammar rules can be learned from training data in a fully automatic fashion. Our SR-TSG parser achieves the state-of-the-art performance on the Wall Street Journal (WSJ) English Penn Tree-bank data.