JOURNAL ARTICLE

Bidirectional Transition-Based Dependency Parsing

Yunzhe YuanYong JiangKewei Tu

Year: 2019 Journal:   Proceedings of the AAAI Conference on Artificial Intelligence Vol: 33 (01)Pages: 7434-7441   Publisher: Association for the Advancement of Artificial Intelligence

Abstract

Transition-based dependency parsing is a fast and effective approach for dependency parsing. Traditionally, a transitionbased dependency parser processes an input sentence and predicts a sequence of parsing actions in a left-to-right manner. During this process, an early prediction error may negatively impact the prediction of subsequent actions. In this paper, we propose a simple framework for bidirectional transitionbased parsing. During training, we learn a left-to-right parser and a right-to-left parser separately. To parse a sentence, we perform joint decoding with the two parsers. We propose three joint decoding algorithms that are based on joint scoring, dual decomposition, and dynamic oracle respectively. Empirical results show that our methods lead to competitive parsing accuracy and our method based on dynamic oracle consistently achieves the best performance.

Keywords:
Computer science Parsing Dependency grammar Parser combinator Artificial intelligence Top-down parsing Decoding methods Natural language processing Sentence Dependency (UML) Oracle Bottom-up parsing Speech recognition Algorithm Programming language

Metrics

10
Cited By
0.92
FWCI (Field Weighted Citation Impact)
49
Refs
0.80
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Natural Language Processing Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence
Text Readability and Simplification
Physical Sciences →  Computer Science →  Artificial Intelligence

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