Multilingual semantic role labeling
Summary, in English
that identify the predicate sense, the arguments of the predicates, and the argument labels. Using these local models, we carried out a beam search to generate a pool of candidates. We then reranked the candidates using a joint learning approach that combines the local models and proposition features.
To address the multilingual nature of the data, we implemented a feature selection procedure that systematically explored the feature space, yielding significant gains over a standard set of features. Our system achieved the second best semantic score overall with an average labeled semantic F1 of 80.31. It obtained the best F1 score on the Chinese and German data
and the second best one on English.
- Department of Computer Science
- Robotics and Semantic Systems
Proceedings of The Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)
- Computer Science