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

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Abstract

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.

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Availability of data and materials

The 498,294 records of the corpus derived from the Shenzhen Address Database are available in Zenodo with the identifiers https://doi.org/10.5281/zenodo.3477007. Complete corpus from the National Address Dataset cannot be made publicly available to protect personal information and to follow the national policy on data security.

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Funding

This work is supported by the Key Cooperation Project of the Chongqing Municipal Education Commission(Grant No. HZ2021008) and Research Project of Graduate Education and Teaching Reform of Chongqing Municipal Education Commission (Grant No. yjg223087).

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  1. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China

    Jianjun Lei, Chen Wu & Ying Wang

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  1. Jianjun Lei
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Correspondence to Jianjun Lei.

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Appendix 1 National dataset example

Appendix 1 National dataset example

Due to privacy issues, the National address dataset cannot be open-sourced. Therefore, we provide some national address data to help readers better understand the content and composition of the National address dataset. For more information, please refer to Table 8.

Table 8 Samples of the national address data
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Lei, J., Wu, C. & Wang, Y. CLGLIAM: contrastive learning model based on global and local semantic interaction for address matching. Appl Intell 53, 29267–29281 (2023). https://doi.org/10.1007/s10489-023-05089-z

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