基于阻塞先验知识的文本层次分类模型

Hierarchical Text Classification Model Based on Blocking Priori Knowledge

文本层次分类中阻塞现象是影响层次分类器性能的重要原因.针对这一问题,提出基于阻塞先验知识的文本层次分类模型.该模型包括两部分:首先对阻塞分布进行估计,提出"阻塞对"识别技术,重点在于获取严重的阻塞方向;其次,把分析出的阻塞先验知识融合到分类过程中,利用层次拓扑结构修正算法,引导阻塞文本"回归"正确分类路径.在中文语料TanCorp上的实验表明,该算法在没有额外增加分类器数目的前提下,能有效改善层次分类性能,是解决层次分类阻塞问题的一种方法.另外,与平面分类算法比较后,该算法更稳定.

Blocking exerts negative effect on the performance of text hierarchical classification. In this paper, a two-step hierarchical text classification model based on blocking priori knowledge is proposed to address the problem. Firstly, blocking distribution is estimated and blocking pair recognition technique focusing on mining the serious blocking direction is presented. Secondly, the hierarchy topology structure is actively refined which attempts to correct misclassification and reduce blocking errors by using blocking priori knowledge. The experimental results on TanCorp, which is a new corpus special for Chinese.text classification, show that the model can improve the performance significantly without increasing the extra number of classifiers and is a method of solving the hierarchical classification blocking problem. In addition, compared with fiat text classification algorithm, this method has stable performance.