Machine learning innovations in address matching: A practical comparison of word2vec and CRFs

• Published in: Transactions in GIS Vol. 23; no. 2; pp. 334 - 348
• Main Authors: Comber, Sam, Arribas‐Bel, Daniel
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.04.2019
• Subjects: Artificial intelligence; Geographic information science; Innovation; Innovations; Learning algorithms; Machine learning; Matching; Records; Spatial data
• ISSN: 1361-1682; 1467-9671
• DOI: 10.1111/tgis.12522

Record linkage is a frequent obstacle to unlocking the benefits of integrated (spatial) data sources. In the absence of unique identifiers to directly join records, practitioners often rely on text‐based approaches for resolving candidate pairs of records to a match. In geographic information science, spatial record linkage is a form of geocoding that pertains to the resolution of text‐based linkage between pairs of addresses into matches and non‐matches. These approaches link text‐based address sequences, integrating sources of data that would otherwise remain in isolation. While recent innovations in machine learning have been introduced in the wider record linkage literature, there is significant potential to apply machine learning to the address matching sub‐field of geographic information science. As a response, this paper introduces two recent developments in text‐based machine learning—conditional random fields and word2vec—that have not been applied to address matching, evaluating their comparative strengths and drawbacks.