微型电脑应用2023，Vol.39Issue(11)：87-90,4.
基于分级信息融合模型的电力投诉工单分类研究
Research on Power Complaint Work Order Classification Based on Hierarchical Information Fusion Model
张莉 1王颖 1赵阳 2崔涵翔 1刘娟1
作者信息
- 1. 国家电网有限公司客户服务中心,天津 300306
- 2. 北京中电普华信息技术有限公司,北京 100031
- 折叠
摘要
Abstract
There are long text data of power customer service tickets,which is a challenge to the construction of the model to classification power customer service tickets.Therefore,this paper proposes a classification model based on hierarchical infor-mation fusion to improve the analysis ability of long text.The Word2vec method is used to process the words in the sentences,and then the word vector and sentence matrix are obtained.The bidirectional long-term and short-term memory network(BiL-STM)is used to learn the dependence between words,and the TextCNN is used to learn the correlation between sentences.The multi-layer perceptron(MLP)is used to extract the deep semantic features,which are learned at all levels to achieve fea-ture layer fusion.The proposed model is tested on a dataset containing thirty thousand real power customer service ticket sam-ples,the average classification accuracy of the five types of service tickets is 0.921,and the average macro-F1 score is 0.901.The results show that compared with TextCNN,BiLSTM,and deep belief network(DBN),the recognition accuracy of the proposed method is improved by 1.9％,5.3％,and 13.5％,respectively,which can give an outstanding performance on the classification of power customer service tickets.关键词
分级信息融合/TextCNN/Word2vec/双向长短时记忆网络Key words
hierarchical information fusion/TextCNN/Word2vec/BiLSTM分类
信息技术与安全科学引用本文复制引用
张莉,王颖,赵阳,崔涵翔,刘娟..基于分级信息融合模型的电力投诉工单分类研究[J].微型电脑应用,2023,39(11):87-90,4.
