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.