基于BootStrapping的集成分类器的中文观点句识别方法

领域相关的大规模和高质量的标注训练数据是分类器性能的重要保证,而标注训练语料是一件费时费力的工作。该文提出了一种采用小规模标注语料识别中文观点句的方法。首先采用Bootstrapping方法扩展训练语料,分别训练贝叶斯、支持向量机和最大熵分类器。最后,通过给三个训练好的分类器赋权获得一个集成分类器。实验结果表明,集成后的分类器性能优于单分类器,并且该方法在使用部分标注训练数据的情况下也能取得与采用全部标注训练数据相近的实验结果。

A Method for Chinese Opinion Sentence Identification Based on the Ensemble Classifier with BootStrapping

The large scale and high quality domain training data is an important guarantee for constructing a high performance classifier. However, it is an expensive work to label a large scale corpus in a domain. In this paper, we propose a method for identifying Chinese opinion sentences using a small-scale labeled corpus. At first, the method uses BootStrapping to expand the small-scale labeled corpus. Using the expanded labeled corpus we then train three classifiers that are based on naive Bayes, support vector machine and maximum entropy respectively. At last, an ensemble classifier is obtained by assigning a set of probability weights to the three trained classifiers. Experimental results indicate that the ensemble classifier is superior to the three single classifiers. And the proposed method can achieve the analogous experimental results by using partially labeled training data or using totally labeled training data.
Key wordsopinion sentence identifying; BootStrapping; ensemble classifier