Chinese Semantic Role Labeling Based on Genetic Algorithm

Authors:
Mao Ning;Shao Yanqiu;Liang Chunxia
Affiliations:
-;-;-
Venue:
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Year:
2012

Citing 3
Cited 0

Automatic labeling of semantic roles

Computational Linguistics
Support Vector Learning for Semantic Argument Classification

Machine Learning
Automatic semantic role labeling for Chinese verbs

IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

To find the effective features from many syntactic features could improve the efficiency and accuracy of the semantic role labeling system. Based on the original semantic role labeling system, the genetic algorithm is used to optimize those syntactic features. From the experiment results, it could be concluded that the syntactic feature selection based on genetic algorithm is efficient. The system uses fewer features, but achieves almost the same F value as the whole extended model.