基于电力工单文本的WTF-IDF特征选择优化

Feature selection optimization of WTF⁃IDF based on power text

文中通过对95598客服服务中心的电力工单历史数据中反映的热点问题进行建模,实现对海量工单文本内容快速、准确地分类,为管理人员发现与解决问题提供支撑。针对电力领域的特殊性,引入了通过新词识别手段构建的针对电力文本的领域词典。通过对传统的TF-IDF算法加以改进,提出了加入领域词典的WTF-IDF特征选择优化算法,旨在给予工单问题中重要的词更多的注意力,有助于机器更好地理解句子中的重点。实验表明,所提方法在电力文本分类方面比传统的TF-IDF取得了更高的准确率与更快的模型训练速度。

This paper models the hot issues reflected in the data of electricity history work orders to improve the speed and accuracy of 95598 customer service on customer judgment.Aiming at the particularity of the power field,a domain dictionary for power text is constructed by means of new word recognition.Through the improvement of the traditional TF⁃IDF algorithm,the WTF⁃IDF feature selection optimization algorithm is proposed to add the important words in the work order problem,which helps the machine better understand the sentence in the focus.Experiments show that the proposed method achieves higher accuracy and faster model training than traditional TF⁃IDF in terms of power text classification.