Paper: Semi-Supervised Feature Transformation for Dependency Parsing

ACL ID D13-1129
Title Semi-Supervised Feature Transformation for Dependency Parsing
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013

In current dependency parsing models, con- ventional features (i.e. base features) defined over surface words and part-of-speech tags in a relatively high-dimensional feature space may suffer from the data sparseness problem and thus exhibit less discriminative power on unseen data. In this paper, we propose a novel semi-supervised approach to address- ing the problem by transforming the base fea- tures into high-level features (i.e. meta fea- tures) with the help of a large amount of au- tomatically parsed data. The meta features are used together with base features in our final parser. Our studies indicate that our proposed approach is very effective in processing un- seen data and features. Experiments on Chi- nese and English data sets show that the fi- nal parser achieves the best-rep...