@inproceedings{han-etal-2020-survey,
title = "A Survey of Unsupervised Dependency Parsing",
author = "Han, Wenjuan and
Jiang, Yong and
Ng, Hwee Tou and
Tu, Kewei",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.227/",
doi = "10.18653/v1/2020.coling-main.227",
pages = "2522--2533",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Survey of Unsupervised Dependency Parsing
%A Han, Wenjuan
%A Jiang, Yong
%A Ng, Hwee Tou
%A Tu, Kewei
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F han-etal-2020-survey
%X 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.
%R 10.18653/v1/2020.coling-main.227
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%P 2522-2533
Markdown (Informal)
[A Survey of Unsupervised Dependency Parsing](https://aclanthology.org/2020.coling-main.227/) (Han et al., COLING 2020)
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.