Automatic labeling of semantic roles
Computational Linguistics
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Joint learning improves semantic role labeling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Generalized inference with multiple semantic role labeling systems
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Improving Chinese semantic role labeling with rich syntactic features
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
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In Semantic Role Labeling (SRL), arguments are usually limited in a syntax subtree. It is reasonable to label arguments locally in such a sub-tree rather than a whole tree. To identify active region of arguments, this paper models Maximal Projection (MP), which is a concept in D-structure from the projection principle of the Principle and Parameters theory. This paper makes a new definition of MP in S-structure and proposes two methods to predict it: the anchor group approach and the single anchor approach. The anchor group approach achieves an accuracy of 87.75% and the single anchor approach achieves 83.63%. Experimental results also indicate that the prediction of MP improves semantic role labeling.