Integration of Multiple Classifiers for Chinese Semantic Dependency Analysis

Authors:
Jiajun Yan;David B. Bracewell;Fuji Ren;Shingo Kuroiwa
Affiliations:
Department of Information Science and Intelligent Systems, The University of Tokushima, Tokushima, Japan;Department of Information Science and Intelligent Systems, The University of Tokushima, Tokushima, Japan;Department of Information Science and Intelligent Systems, The University of Tokushima, Tokushima, Japan and School of Information Engineering, Beijing University of Posts and Telecommunications, ...;Department of Information Science and Intelligent Systems, The University of Tokushima, Tokushima, Japan
Venue:
Electronic Notes in Theoretical Computer Science (ENTCS)
Year:
2009

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Abstract

Semantic Dependency Analysis (SDA) has extensive applications in Natural Language Processing (NLP). In this paper, an integration of multiple classifiers is presented for SDA of Chinese. A Naive Bayesian Classifier, a Decision Tree and a Maximum Entropy classifier are used in a majority wins voting scheme. A portion of the Penn Chinese Treebank was manually annotated with semantic dependency structure. Then each of the three classifiers was trained on the same training data. All three of the classifiers were used to produce candidate relations for test data and the candidate relation that had the majority vote was chosen. The proposed approach achieved an accuracy of 86% in experimentation, which shows that the proposed approach is a promising one for semantic dependency analysis of Chinese.