概念推理网及其在文本分类中的应用

在分析了当前文本分类中学常用方法的基础上，提出了一种新的分类模型，该模型是对人的分类过程的一种模拟，在已的有英语语义词典及大量训练集的基础上，应用机器学习、数据挖掘等技术进行知识获取并最终形成若干个概念推理网，对待分类的文档可以激活相应的网络，网时传播推理以决定其类别的归属，试验表明：该方法具有较高的分类正确率与召回率。

THE CONCEPT-REASONING NETWORK AND ITS APPLICATION IN TEXT CLASSIFICATION

Following the analysis of the current text classification methods, a new classification model is proposed in this paper. The key of the model is to simulate the process of human classification. Machine learning and data mining techniques are applied to acquire knowledge and build a concept reasoning network based on semantic dictionary and large training set. Given a document, this model can activate several networks and then reason to decide which classification it belongs to. The experiment indicates that the model has high classification precision and recall.