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Review

Automatic Identification of Addresses: A Systematic Literature Review

NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
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Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2022, 11(1), 11; https://doi.org/10.3390/ijgi11010011
Received: 16 November 2021 / Revised: 23 December 2021 / Accepted: 26 December 2021 / Published: 29 December 2021
Address matching continues to play a central role at various levels, through geocoding and data integration from different sources, with a view to promote activities such as urban planning, location-based services, and the construction of databases like those used in census operations. However, the task of address matching continues to face several challenges, such as non-standard or incomplete address records or addresses written in more complex languages. In order to better understand how current limitations can be overcome, this paper conducted a systematic literature review focused on automated approaches to address matching and their evolution across time. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, resulting in a final set of 41 papers published between 2002 and 2021, the great majority of which are after 2017, with Chinese authors leading the way. The main findings revealed a consistent move from more traditional approaches to deep learning methods based on semantics, encoder-decoder architectures, and attention mechanisms, as well as the very recent adoption of hybrid approaches making an increased use of spatial constraints and entities. The adoption of evolutionary-based approaches and privacy preserving methods stand as some of the research gaps to address in future studies. View Full-Text
Keywords: address matching; address parsing; machine learning; deep learning; natural language processing; address geocoding address matching; address parsing; machine learning; deep learning; natural language processing; address geocoding
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MDPI and ACS Style

Cruz, P.; Vanneschi, L.; Painho, M.; Rita, P. Automatic Identification of Addresses: A Systematic Literature Review. ISPRS Int. J. Geo-Inf. 2022, 11, 11. https://doi.org/10.3390/ijgi11010011

AMA Style

Cruz P, Vanneschi L, Painho M, Rita P. Automatic Identification of Addresses: A Systematic Literature Review. ISPRS International Journal of Geo-Information. 2022; 11(1):11. https://doi.org/10.3390/ijgi11010011

Chicago/Turabian Style

Cruz, Paula, Leonardo Vanneschi, Marco Painho, and Paulo Rita. 2022. "Automatic Identification of Addresses: A Systematic Literature Review" ISPRS International Journal of Geo-Information 11, no. 1: 11. https://doi.org/10.3390/ijgi11010011

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