Dependency Language Models for Transition-based Dependency Parsing

Juntao Yu, Bernd Bohnet


Abstract
In this paper, we present an approach to improve the accuracy of a strong transition-based dependency parser by exploiting dependency language models that are extracted from a large parsed corpus. We integrated a small number of features based on the dependency language models into the parser. To demonstrate the effectiveness of the proposed approach, we evaluate our parser on standard English and Chinese data where the base parser could achieve competitive accuracy scores. Our enhanced parser achieved state-of-the-art accuracy on Chinese data and competitive results on English data. We gained a large absolute improvement of one point (UAS) on Chinese and 0.5 points for English.
Anthology ID:
W17-6302
Volume:
Proceedings of the 15th International Conference on Parsing Technologies
Month:
September
Year:
2017
Address:
Pisa, Italy
Venues:
IWPT | WS
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
11–17
Language:
URL:
https://aclanthology.org/W17-6302
DOI:
Bibkey:
Cite (ACL):
Juntao Yu and Bernd Bohnet. 2017. Dependency Language Models for Transition-based Dependency Parsing. In Proceedings of the 15th International Conference on Parsing Technologies, pages 11–17, Pisa, Italy. Association for Computational Linguistics.
Cite (Informal):
Dependency Language Models for Transition-based Dependency Parsing (Yu & Bohnet, 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-6302.pdf
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