Support Vector Learning for Semantic Argument Classification
Machine Learning
Joint learning improves semantic role labeling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Semantic role labeling using dependency trees
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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
Combination strategies for semantic role labeling
Journal of Artificial Intelligence Research
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
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In this paper, we describe a syntactic and semantic dependency parsing system submitted to the shared task of CoNLL 2008. The proposed system consists of five modules: syntactic dependency parser, predicate identifier, local semantic role labeler, global role sequence candidate generator, and role sequence selector. The syntactic dependency parser is based on Malt Parser and the sequence candidate generator is based on CKY style algorithm. The remaining three modules are implemented by using maximum entropy classifiers. The proposed system achieves 76.90 of labeled F1 for the overall task, 84.82 of labeled attachment, and 68.71 of labeled F1 on the WSJ+Brown test set.