Shift-Reduce Constituent Parsing with Neural Lookahead Features

Transition-based models can be fast and accurate for constituent parsing. Compared with chart-based models, they leverage richer features by extracting history information from a parser stack, which consists of a sequence of non-local constituents. On the other hand, during incremental parsing, cons...

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Bibliographic Details
Published in:Transactions of the Association for Computational Linguistics 2017-01, Vol.5, p.45-58
Main Authors: Liu, Jiangming, Zhang, Yue
Format: Article
Language:English
Subjects:
Computational linguistics
Constituents
Linguistics
Parsers
Parsing
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Summary:Transition-based models can be fast and accurate for constituent parsing. Compared with chart-based models, they leverage richer features by extracting history information from a parser stack, which consists of a sequence of non-local constituents. On the other hand, during incremental parsing, constituent information on the right hand side of the current word is not utilized, which is a relative weakness of shift-reduce parsing. To address this limitation, we leverage a fast neural model to extract lookahead features. In particular, we build a bidirectional LSTM model, which leverages full sentence information to predict the hierarchy of constituents that each word starts and ends. The results are then passed to a strong transition-based constituent parser as lookahead features. The resulting parser gives 1.3% absolute improvement in WSJ and 2.3% in CTB compared to the baseline, giving the highest reported accuracies for fully-supervised parsing.
ISSN:2307-387X
2307-387X
DOI:10.1162/tacl_a_00045