A Survey of Unsupervised Dependency Parsing

Wenjuan Han, Yong Jiang, Hwee Tou Ng, Kewei Tu

Abstract
Syntactic dependency parsing is an important task in natural language processing. Unsupervised dependency parsing aims to learn a dependency parser from sentences that have no annotation of their correct parse trees. Despite its difficulty, unsupervised parsing is an interesting research direction because of its capability of utilizing almost unlimited unannotated text data. It also serves as the basis for other research in low-resource parsing. In this paper, we survey existing approaches to unsupervised dependency parsing, identify two major classes of approaches, and discuss recent trends. We hope that our survey can provide insights for researchers and facilitate future research on this topic.
Anthology ID:
2020.coling-main.227
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2522–2533
Language:
URL:
https://aclanthology.org/2020.coling-main.227
DOI:
10.18653/v1/2020.coling-main.227
Bibkey:
Cite (ACL):
Wenjuan Han, Yong Jiang, Hwee Tou Ng, and Kewei Tu. 2020. A Survey of Unsupervised Dependency Parsing. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2522–2533, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
A Survey of Unsupervised Dependency Parsing (Han et al., COLING 2020)
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https://preview.aclanthology.org/auto-file-uploads/2020.coling-main.227.pdf
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