COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Automatic labeling of semantic roles
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Support Vector Learning for Semantic Argument Classification
Machine Learning
Maximum entropy models for FrameNet classification
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
FrameNet-based semantic parsing using maximum entropy models
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Semantic role labeling via integer linear programming inference
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
A hybrid convolution tree kernel for semantic role labeling
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Towards robust semantic role labeling
Computational Linguistics
Using a Hybrid Convolution Tree Kernel for Semantic Role Labeling
ACM Transactions on Asian Language Information Processing (TALIP)
Semantic Role Labeling of NomBank: a maximum entropy approach
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Improving Chinese semantic role classification with hierarchical feature selection strategy
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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This paper describes our research on automatic semantic argument classification, using the PropBank data [Kingsbury et al., 2002]. Previous research employed features that were based either on a full parse or shallow parse of a sentence. These features were mostly based on an individual semantic argument and the relation between the predicate and a semantic argument, but they did not capture the interdependence among all arguments of a predicate. In this paper, we propose the use of the neighboring semantic arguments of a predicate as additional features in determining the class of the current semantic argument. Our experimental results show significant improvement in the accuracy of semantic argument classification after exploiting argument interdependence. Argument classification accuracy on the standard Section 23 test set improves to 90.50%, representing a relative error reduction of 18%.