CLC number: TN 918

A Short Text Classification Model for Electrical Equipment Defects Based on Contextual Features

¹ School of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
² CSG Smart Science & Technology Co., LTD, Shanghai 201203, China
³ State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China

^† To whom correspondence should be addressed. E-mail: zenggh@sues.edu.cn

Received: 11 September 2022

Abstract

The defective information of substation equipment is usually recorded in the form of text. Due to the irregular spoken expressions of equipment inspectors, the defect information lacks sufficient contextual information and becomes more ambiguous. To solve the problem of sparse data deficient of semantic features in classification process, a short text classification model for defects in electrical equipment that fuses contextual features is proposed. The model uses bi-directional long-short term memory in short text classification to obtain the contextual semantics of short text data. Also, the attention mechanism is introduced to assign weights to different information in the context. Meanwhile, this model optimizes the convolutional neural network parameters with the help of the genetic algorithm for extracting salient features. According to the experimental results, the model can effectively realize the classification of power equipment defect text. In addition, the model was tested on an automotive parts repair dataset provided by the project partners, thus enabling the effective application of the method in specific industrial scenarios.