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基于BERT-BiLSTM模型的输水工程巡检文本智能分类

Text Classification of Water Diversion Project Inspection Text Based on BERT-BiLSTM Model

  • 摘要: 长距离输水工程线路长,沿线环境复杂,在输水工程日常运行过程中,工程安全巡检是维护生产安全的重要手段。在工程巡检中产生了大量的巡检文本数据。在传统生产管理过程中,巡检文本依赖于管理人员人工按照出现问题的严重程度进行分类,效率低下且容易出现主观性问题分类出错,不足以良好管理长线路,沿线环境复杂的输水工程。针对这一问题,提出一种结合双向长短期记忆神经网络(Bi-directional Long Short-Term Memory)和BERT神经网络的混合深度学习模型对巡检文本智能分类方法,模型使用BERT作为输入层将巡检文本转化为特征向量,再将结果输入到BiLSTM模型挖掘文本特征,实现巡检文本智能分类。以南水北调中线巡检文本为算例,实验结果表明:该模型与主流深度学习模型文本卷积神经网络(TextCNN),BERT,BiLSTM模型相比,准确率、召回率和F1值分别达到92.30%、92.32%、92.30%,模型性能优于其他深度学习模型。

     

    Abstract: Long-distance water transmission project lines, complex environment along the line, in the daily operation of water transmission projects, engineering safety inspection is an important way to maintain production safety. A large amount of inspection text data is generated in the engineering inspection. In the traditional production management process, inspection text relies on managers to manually classify according to the severity of the problem, which is inefficient and prone to subjective problem classification errors, and is not sufficient for good management of long lines and complex environment along the water transmission project. To address this problem, this paper proposes a hybrid deep learning model combining Bi-directional Long Short-Term Memory(BERT) and BERT neural network to classify inspection text intelligently by using BERT as the input layer to transform inspection text into feature vectors, and then inputting the results to BiLSTM.The model uses BERT as the input layer to transform inspection text into feature vectors, and then feeds the results into BiLSTM model to mine text features and realize intelligent classification of inspection text. Compared with the mainstream deep learning models TextCNN, BERT and BiLSTM, the accuracy, recall and F1 values of the model reach 92.30%, 92.32% and 92.30% respectively, and the model outperforms other deep learning models.

     

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