Chinese Base NP Chunking by Error-driven Combination Classifiers
Fang Xu, Chengqing Zong, Xia Wang · Zhongwen xinxi xuebao · 2007
This paper proposes a hybrid error-driven combination approach to chunking Chinese Base noun phrase(Chinese Base NP),which combines TBL(Transformation-based Learning) model and CRF(Conditional Random Field) model.First,we give an overview of the Chinese and English Base NP chunking,followed by a description of the Chinese Base NP chunking task.In order to analyze the results respectively from the two(TBL-based and CRF-based) classifiers and improve the performance of the Base NP chunkers,an error-driven SVM(Support Vector Machine) based classifier is trained from the classification errors of the two classifiers.According to our experiments,the hybrid method achieves the best results with F-measure of 89.72% and improves by 2.35% in the best case compared with other methods.