基于概念与词根双特征互助文本分类模型

摘要

传统半监督文本分类方法，大多数建立在词根特征的基础上，忽略了语义特征的重要性，导致分类精度不高。考虑到语义对分类的影响，本文提出融合概念与词根双特征的文本分类模型。该方法以WordNet为本体库，在Co-training框架下，构造基于概念和词根的双分类器进行协同训练的分类模型。实验分析了新模型分类准确率和召回率，结果显示新模型相对于旧模型在这2方面都有提升，表明基于概念与词根双特征互助的新算法具有更高的有效性。%Traditional semi-supervised text classification methods were built based on the features of root, however, the common disadvantage of neglecting the importance of semantic features resulted in low precision of classification.In order to take account of the influence of semantic on classification, a text classification model comprehensively making use of dual features of concept and root was brought forward.Under the framework of cooperative training, this algorithm considered WordNet as ontology library and built double classifiers based on both concept and root for cooperative training.Through experiments, we analyzed the accura-cy rate and recall rate of new classification model, and the results showed the promotions of both accuracy rate and recall rate in new model comparing with old model.It indicates that the new algorithm based on cooperation of dual features of concept and root is more effective than the old algorithm.