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.
BibTeX
@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"
}