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Multi-task deep learning model based on hierarchical relations of address elements for semantic address matching

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Abstract

Address matching, which aims to match unstructured addresses with standard addresses in an address database, is a key part of geocoding. The core problem of address matching corresponds to text matching in natural language processing. Existing rule-based methods require human-designed templates and thus, have limited applicability. Machine learning and deep learning-based methods ignore the hierarchical relations between address elements, which easily misclassify semantically similar but geographically different locations. We note that the hierarchy of address elements can fill the semantic gap in address matching. Inspired by how humans discriminate addresses, we propose a multi-task learning approach. The approach jointly recognises the address elements and matches the addresses to incorporate the hierarchical relations between the address elements into the neural network. Simultaneously, we introduce a priori information on the hierarchical relationship of address elements through the conditional random field model. Experimental results on the benchmark datasets Shenzhen Address Database and Jiangsu-Hunan Address Dataset demonstrate the effectiveness of our approach. We achieved state-of-the-art F1 scores (i.e. the harmonic mean of precision and recall) of 99.0 and 94.2 on the two datasets, respectively.

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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 Jiangsu–Hunan Address Dataset cannot be made publicly available to protect personal information and to follow the national policy on data security.

Code availability

The codes that support the findings of this study are available with the identifier(s) at the private link: https://figshare.com/s/a815fddc2429d4bd6cb2.

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Funding

This research is supported by National Natural Science Foundation of China [62172449, 71790615, 62006251, 62172441], Hunan Provincial Natural Science Foundation of China [2021JJ30870, 2021JJ40783, 2020JJ4746], Changsha Municipal Natural Science Foundation [kq2014134] and National Key Research and Development Program of China [2020YFC0832700]. This work was supported in part by the High Performance Computing Center of Central South University.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, China

    Fangfang Li, Yiheng Lu, Junwen Duan & Xiyao Liu

  2. Institute of Big Data and Internet Innovation, Hunan University of Technology and Business, Changsha, China

    Xingliang Mao

Authors
  1. Fangfang Li
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  2. Yiheng Lu
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  3. Xingliang Mao
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  4. Junwen Duan
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  5. Xiyao Liu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by FL, YL, XM, JD and XL. The first draft of the manuscript was written by YL, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Xingliang Mao.

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Li, F., Lu, Y., Mao, X. et al. Multi-task deep learning model based on hierarchical relations of address elements for semantic address matching. Neural Comput & Applic 34, 8919–8931 (2022). https://doi.org/10.1007/s00521-022-06914-1

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