CLGLIAM: contrastive learning model based on global and local semantic interaction for address matching

As an essential part of geocoding, address matching has gained increasing research attention. Due to the long-distance dependency and unstructured property, existing address-matching methods hardly capture the contextual and implicit semantic information of unstructured text addresses. This paper presents a Contrastive Learning model based on Global and Local representation Interaction for Address Matching (referred to as CLGLIAM), which introduces a novel global and local interaction network to enhance the discrimination ability of the model on the hard negative address by associating the relationship between the global and local address representation explicitly. Simultaneously, to improve the generalization and transferability of the model, we utilize contrastive learning to enrich the data sample and extricate the model from task-specific knowledge. Furthermore, extensive experiments are conducted on Shenzhen and national address datasets to verify the effectiveness of our approach. Our model achieves state-of-the-art F1 scores of 99.26 and 98.50 on the two datasets, respectively. And the extended hard negative experiments further demonstrate the better performance of CLGLIAM in terms of semantic discrimination.