Paper: Forest-guided Supertagger Training

ACL ID C10-1144
Title Forest-guided Supertagger Training
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010

Supertagging is an important technique for deep syntactic analysis. A super- tagger is usually trained independently of the parser using a sequence labeling method. This presents an inconsistent training objective between the supertagger and the parser. In this paper, we pro- pose a forest-guided supertagger training method to alleviate this problem by incor- porating global grammar constraints into the supertagging process using a CFG- filter. It also provides an approach to make the supertagger and the parser more tightly integrated. The experiment shows that using the forest-guided trained super- tagger, the parser got an absolute 0.68% improvement from baseline in F-score for predicate-argument relation recogni- tion accuracy and achieved a competi- tive result of 89.31% with a faster ...