An intelligent work order classification model for government service based on multi-label neural network

Abstract

The classification of work orders for livelihood service play an important role in the e-government system, which goal is to push these work orders to the corresponding processing department. Although many studies have realized the classification of work orders automatically, these work orders are either related to many subjects or related to many departments. Therefore, the problem of work order classification is not a single label classification problem, but a multi-label classification problem. In view of the continuous development of deep neural networks, the goal of this paper is to modify a MultilabelMarginLoss function and design a MultilabelCrossEntropyLoss function to realize the multi-classification system of work orders, which can push the work orders to the related departments(one or more departments) automatically according to their appeal contents (consisting of short texts). We perform deep neural network for text classification including TextCNN, TextRCNN, TextRNN, TextRNN_Att, FastText and DPCNN on the real government hotline data set and compare the performances of these models. The results reveal that TextCNN and TextRCNN have a more stable performance on different loss functions, especially on the MultilabelCrossEntropyLoss function.