BiLSTM_DPCNN模型在电力客服工单数据分类中的应用

Application of BiLSTM_DPCNN Model in Work Order Data Classification for Power Customer Service

电力客服工单数据以文本形式记录电力用户的需求信息,合理的工单分类方法有利于准确定位用户需求,提升电力系统的运行效率.针对工单数据特征稀疏、依赖性强等问题,本文对基于字符级嵌入的长短时记忆网络(Bidirectional Long Short-Term Memory network,BiLSTM)和卷积神经网络(Convolution Neural Network,CNN)组合的结构模型进行优化.该模型首先对Word2Vec模型训练的词向量进行降噪处理,得到文本的特征表示;其次,利用BiLSTM网络递归地学习文本的时序信息,提取句子特征信息;再输入到双通道池化的CNN网络中,进行局部的特征提取.通过在真实客服工单数据集上的测试实验,验证了该模型在客服工单分类任务上的具有较好的精确性和鲁棒性.

The power customer service order data records the demand of power users in text.A reasonable work order classification method is helpful to accurately identify the demand of users and improve the operating efficiency of the power system.To solve the problems of sparse feature data and strong dependency of work order data,this study optimizes the structural model that combines character-level embedded Bidirectional Long-Short-Term Memory network(BiLSTM)and Convolution Neural Network(CNN).Firstly,this model obtains the feature representation of text by noise reduction on the term vectors trained by the Word2Vec model.Secondly,it uses the BiLSTM network to recursively learn the time sequence information of the text to extract the feature information of sentences.Finally,those obtained are input into the double-channel pooled CNN for the extraction of local features.The test experiments on the real work order data set of power customer service demonstrate that the model has good accuracy and robustness in the task of classifying work orders of power customer service.