Article
Version 1
Preserved in Portico This version is not peer-reviewed
Research on the Classification of Work Orders based on BERT and Feature Fusion
Version 1
: Received: 7 June 2024 / Approved: 10 June 2024 / Online: 11 June 2024 (08:07:15 CEST)
A peer-reviewed article of this Preprint also exists.
Xiong, Y.; Chen, G.; Cao, J. Research on Public Service Request Text Classification Based on BERT-BiLSTM-CNN Feature Fusion. Appl. Sci. 2024, 14, 6282. Xiong, Y.; Chen, G.; Cao, J. Research on Public Service Request Text Classification Based on BERT-BiLSTM-CNN Feature Fusion. Appl. Sci. 2024, 14, 6282.
Abstract
Convolutional neural networks (CNNs) face challenges in capturing long-distance text correlations, and Bidirectional Long Short-Term Memory (BiLSTM) networks exhibit limited feature extraction capabilities for text classification of work order. To address the abovementioned problems, this work utilizes an ensemble learning approach to integrate model elements efficiently. This study presents a method for classifying work order texts using a hybrid neural network model called BERT-BiLSTM-CNN. First, use Bert for preprocessing to obtain text vector representations. Then, capture context and process sequence information through BiLSTM. Next, capture local features in the text through CNN. Finally, obtain classification results through Softmax. Through comparative analysis, the method of fusing these three models is superior to other hybrid neural network model architectures in multiple classification tasks. It has a significant effect on work order classification.
Keywords
Text classification; Ensemble learning; Hybrid neural network model; Feature fusion; BERT
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
