Abstract
In e-commerce logistics, government registration, financial transportation and other fields, communication addresses are required. Analyzing the communication address is crucial. There are various challenges in address recognition due to the address text’s features of free writing, numerous aliases and significant text similarity. This study shows an ENEX-FP address recognition model, which consists of an entity extractor (ENEX) and a feature processor (FP) for address recognition, as a solution to the issues mentioned. This study uses adversarial training to enhance the model’s robustness and a hierarchical learning rate setup and learning rate attenuation technique to enhance recognition accuracy. Compared with traditional named entity recognition models, our model achieves an F1-score of 93.47% and 94.59% in the dataset, demonstrating the ENEX-FP model’s effectiveness in recognizing addresses.
Funder
Plan of Youth Innovation Team Development of Colleges and Universities in Shandong Province
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference31 articles.
1. A survey of named entity recognition and classification;Nadeau;Int. J. Linguist. Lang. Resour.,2007
2. Tang, X., Huang, Y., Xia, M., and Long, C. (2022). A Multi-Task BERT-BiLSTM-AM-CRF Strategy for Chinese Named Entity Recognition. Neural Process. Lett., 1–21.
3. Electrocardiogram soft computing using hybrid deep learning CNN-ELM;Zhou;Appl. Soft Comput.,2020
4. Li, J., Sun, A., Han, J., and Li, C. (2018). A Survey on Deep Learning for Named Entity Recognition. arXiv.
5. Zou, H., Liu, H., Zhou, T., Jiashun, L., and Zhan, Y. (2020, January 16–18). Short-Term Traffic Flow Prediction using DTW-BiGRU Model. Proceedings of the 2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC), Zhanjiang, China.
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