# A Survey of Unsupervised Dependency Parsing

arXiv: https://arxiv.org/abs/2010.01535 · HTML: https://mlsanity.com/paper/2010.01535/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Neural Bi-Lexicalized PCFG Induction](https://mlsanity.com/paper/2105.15021/) (2021)
- [Ensemble Distillation for Unsupervised Constituency Parsing](https://mlsanity.com/paper/2310.01717/) (2023)
- [Who is the root in a syntactic dependency structure?](https://mlsanity.com/paper/2501.15188/) (2025)
- [Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency](https://mlsanity.com/paper/2305.20018/) (2023)
- [Understanding Syntactic Generalization in Structure-inducing Language Models](https://mlsanity.com/paper/2508.07969/) (2025)
- [On the feasibility of dependency parsing of non-human sequences without a gold standard. Is evaluation possible in other species?](https://mlsanity.com/paper/2607.06542/) (2026)

## Abstract (arXiv)

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.