Making large-scale support vector machine learning practical
Advances in kernel methods
Automatic labeling of semantic roles
Computational Linguistics
Support Vector Learning for Semantic Argument Classification
Machine Learning
Annotating the propositions in the Penn Chinese Treebank
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
Joint learning improves semantic role labeling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Towards robust semantic role labeling
Computational Linguistics
Improving Chinese semantic role classification with hierarchical feature selection strategy
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Automatic semantic role labeling for Chinese verbs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Brutus: a semantic role labeling system incorporating CCG, CFG, and dependency features
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
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This paper proposes a novel corpus-based method for feature-based semantic role labeling (SRL). The method first constructs a number of combined features based on basic features and can rapidly discern the discriminative combined features that will improve the performance of SRL. According to the distribution in the corpus, we define a statistical quantity that can efficiently measure the classifying capacity of the combining feature, and then retain the high-value combined features for the later classification. The experiments on Chinese Proposition Bank (CPB) corpus show the method can improve the F-score of SRL by more than one percent.