基于词向量语义扩展的网络文本特征选择方法研究

Feature Selection Method of the Network Text Based on Semantic Extension with Word Vector

【目的/意义】现有特征选择方法可以计算特征所携带的类别信息量,但却不能计算出与主题相关的语义信息。针对其不足,为了提高分类准确率,提出一种基于词向量语义扩展的特征选择方法。【方法/过程】该方法引入了词向量进行主题语义计算,筛选出具有类别语义信息的词条实现特征候选集的特征扩展,并采用K近邻分类方法进行分类实验。【结果/结论】实验结果显示新方法能够有效提取主题语义特征并提高分类准确率。

【Purpose/significance】The feature selection methods existed can calculate the information amount of category,but can not calculate the semantic information on the subject. Aiming at the disadvantages, in order to improve the classification accuracy, a word vector selection method based on semantic extension is proposed.【Method/process】The word vector calculation is introduced in this method, which can get category semantic words for features set expansion. K nearest neighbor classification is used to run classification experiment.【Resultconclusion】Experimental results show that the new method can effectively supplement semantic features about topic and improve classification acuracy.