Information extraction from green channel textual records on expressways using hybrid deep learning.

The expressway green channel is an essential transportation policy for moving fresh agricultural products in China. In order to extract knowledge from various records, this study presents a cutting-edge approach to extract information from textual records of failure cases in the vertical field of expressway green channel. We proposed a hybrid approach based on BIO labeling, pre-trained model, deep learning and CRF to build a named entity recognition (NER) model with the optimal prediction performance. Eight entities are designed and proposed in the NER processing for the expressway green channel. three typical pre-trained natural language processing models are utilized and compared to recognize entities and obtain feature vectors, including bidirectional encoder representations from transformer (BERT), ALBERT, and RoBERTa. An ablation experiment is performed to analyze the influence of each factor on the proposed models. Used the survey data from the expressway green channel management system in Shaanxi Province of China, the experimental results show that the precision, recall, and F1-score of the RoBERTa-BiGRU-CRF model are 93.04%, 92.99%, and 92.99%, respectively. As the results, it is discovered that the text features extracted from pre-training substantially enhance the prediction accuracy of deep learning algorithms. Surprisingly, the RoBERTa model is highly effective in the task for the expressway green channel NER. This study provides a timely and necessary knowledge extraction on the Expressway Green Channel in terms of textual data, offering a systematical explanation of failure cases and valuable insights for future research.