A Survey of Unsupervised Dependency Parsing
This is an incremental survey paper that summarizes methods for a domain-specific problem in natural language processing, targeting researchers in low-resource parsing.
The paper surveys existing approaches to unsupervised dependency parsing, which aims to learn parsers from unannotated text, identifying two major classes and discussing recent trends to provide insights for researchers.
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.