Co-training机器学习方法在中文组块识别中的应用

采用半指导机器学习方法co2training 实现中文组块识别。首先明确了中文组块的定义,co-training 算法的形式化定义。文中提出了基于一致性的co-training 选取方法将增益的隐马尔可夫模型(Transductive HMM) 和基于转换规则的分类器(fnTBL) 组合成一个分类体系,并与自我训练方法进行了比较,在小规模汉语树库语料和大规模未带标汉语语料上进行中文组块识别,实验结果要比单纯使用小规模的树库语料有所提高,F 值分别达到了85134 %和83141 % ,分别提高了2113 %和7121 %。

Chinese Text Chunking Using Co-training Method

In this paper we discuss the application of semi-supervised machine learning method-co-training on Chinese Text Chunking. Firstly, we give the definition of Chinese chunk,then the formalized definition of co-training algorithm.We proposed a example selection method based on the consistence, using two classifiers : Transductive HMM and fnTBL to combine a classification system to perform the Chinese text chunking task with the small-scale labled Chinese treebank and large-scale unlabled Chinese corpus. The result were compared with the self-training result and the result of the non co-training experiment in which we only used the small-scale Chinese treebank as training data and use one classifier(Transductive HMM or fnTBL) to recognize the Chinese chunk. The improvement is significant, the F value of the two classifiers reached 83.41%,85.34%, get a improvement of 2.13 points and 7.21 points respectively.