PostMatch: A Framework for Efficient Address Matching

  • Authors
  • Estivill-Castro, Vladimir
  • UPF authors
  • ESTIVILL CASTRO, VLADIMIR;
  • Authors of the book
  • Yue XuRosalind WangAnton Lord Yee Ling Boo Richi Nayak Yanchang Zhao Graham William
  • Book title
  • Data Mining. AusDM 2021
  • Publisher
  • Springer-Verlag
  • Publication year
  • 2021
  • Pages
  • 136-151
  • ISBN
  • 9789811685309
  • Abstract
  • Matching lists of addresses is an increasingly common task executed by business and governments alike. However, due to security issues, this task cannot always be performed using cloud computing. Moreover, addresses can arrive with spelling errors that can cause non-matches or `false negatives¿ to occur. Our proposed framework, PostMatch, provides a locally-executed method for address-matching that combines the open-source `Libpostal¿ address-parsing library with our `postparse¿ post-processor code and machine-learning. PostMatch provides improved parsing accuracy compared with Libpostal alone, approaching 96.9%. The matching process features the Jaro-Winkler edit distance algorithm together with XGBoost machine-learning to achieve very high accuracy on public data. PostMatch is open-source (GPL3 licensed) and available as R script code on Github.
  • Complete citation
  • Estivill-Castro, Vladimir. PostMatch: A Framework for Efficient Address Matching. In: Yue XuRosalind WangAnton Lord Yee Ling Boo Richi Nayak Yanchang Zhao Graham William. Data Mining. AusDM 2021. 1 ed. Springer-Verlag; 2021. p. 136-151.