Automatic labeling of semantic roles
Computational Linguistics
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Adding semantic roles to the chinese treebank
Natural Language Engineering
The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Dependency-based syntactic-semantic analysis with PropBank and NomBank
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Mixing and blending syntactic and semantic dependencies
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
The CoNLL-2009 shared task: syntactic and semantic dependencies in multiple languages
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
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I describe a fast multilingual parser for semantic dependencies. The parser is implemented as a pipeline of linear classifiers trained with support vector machines. I use only first order features, and no pair-wise feature combinations in order to reduce training and prediction times. Hyper-parameters are carefully tuned for each language and sub-problem. The system is evaluated on seven different languages: Catalan, Chinese, Czech, English, German, Japanese and Spanish. An analysis of learning rates and of the reliance on syntactic parsing quality shows that only modest improvements could be expected for most languages given more training data; Better syntactic parsing quality, on the other hand, could greatly improve the results. Individual tuning of hyper-parameters is crucial for obtaining good semantic parsing quality.