Multilingual Dependency Parsing Using Global Features
Tetsuji Nakagawa · 2007
In this paper, we describe a two-stage multilingual dependency parser used for the multilingual track of the CoNLL 2007 shared task. The system consists of two components: an unlabeled dependency parser using Gibbs sampling which can incorporate sentence-level (global) features as well as token-level (local) features, and a dependency relation labeling module based on Support Vector Machines. Experimental results show that the global features are useful in all the languages. 1