Paper: Multilingual Dependency Parsing Using Global Features

ACL ID D07-1100
Title Multilingual Dependency Parsing Using Global Features
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

In this paper, we describe a two-stage multi- lingual dependency parser used for the mul- tilingual track of the CoNLL 2007 shared task. The system consists of two compo- nents: an unlabeled dependency parser us- ing Gibbs sampling which can incorporate sentence-level (global) features as well as token-level (local) features, and a depen- dency relation labeling module based on Support Vector Machines. Experimental re- sults show that the global features are useful in all the languages.