Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06

2006 ◽  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Empirical Methods

Improving shift-reduce constituency parsing with large-scale unlabeled data

2013 ◽  
Vol 21 (1) ◽  
pp. 113-138 ◽  
Author(s):  
MUHUA ZHU ◽  
JINGBO ZHU ◽  
HUIZHEN WANG
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
State Of The Art ◽  
Unlabeled Data ◽  
Experimental Results ◽  
Empirical Methods ◽  
Part Of Speech

AbstractShift-reduce parsing has been studied extensively for diverse grammars due to the simplicity and running efficiency. However, in the field of constituency parsing, shift-reduce parsers lag behind state-of-the-art parsers. In this paper we propose a semi-supervised approach for advancing shift-reduce constituency parsing. First, we apply the uptraining approach (Petrov, S. et al. 2010. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP), Cambridge, MA, USA, pp. 705–713) to improve part-of-speech taggers to provide better part-of-speech tags to subsequent shift-reduce parsers. Second, we enhance shift-reduce parsing models with novel features that are defined on lexical dependency information. Both stages depend on the use of large-scale unlabeled data. Experimental results show that the approach achieves overall improvements of 1.5 percent and 2.1 percent on English and Chinese data respectively. Moreover, the final parsing accuracies reach 90.9 percent and 82.2 percent respectively, which are comparable with the accuracy of state-of-the-art parsers.

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