Paper: Improving Unsupervised Dependency Parsing with Richer Contexts and Smoothing

ACL ID N09-1012
Title Improving Unsupervised Dependency Parsing with Richer Contexts and Smoothing
Venue Human Language Technologies
Session Main Conference
Year 2009
Authors

Unsupervised grammar induction models tend to employ relatively simple models of syntax when compared to their supervised counter- parts. Traditionally, the unsupervised mod- els have been kept simple due to tractabil- ity and data sparsity concerns. In this paper, we introduce basic valence frames and lexi- cal information into an unsupervised depen- dency grammar inducer and show how this additional information can be leveraged via smoothing. Our model produces state-of-the- art results on the task of unsupervised gram- mar induction, improving over the best previ- ous work by almost 10 percentage points.