A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching

Mariona Coll Ardanuy, Kasra Hosseini, Katherine McDonough, Amrey Krause, Daniel Van Strien, Federico Nanni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Recognizing toponyms and resolving them to their real-world referents is required to provide advanced semantic access to textual data. This process is often hindered by the high degree of variation in toponyms. Candidate selection is the task of identifying the potential entities that can be referred to by a previously recognized toponym. While it has traditionally received little attention, candidate selection has a significant impact on downstream tasks (i.e. entity resolution), especially in noisy or non-standard text. In this paper, we introduce a deep learning method for candidate selection through toponym matching, using state-of-the-art neural network architectures. We perform an intrinsic toponym matching evaluation based on several datasets, which cover various challenging scenarios (cross-lingual and regional variations, as well as OCR errors) and assess its performance in the context of geographical candidate selection in English and Spanish.

Original languageEnglish
Title of host publicationProceedings of the 28th International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020
EditorsChang-Tien Lu, Fusheng Wang, Goce Trajcevski, Yan Huang, Shawn Newsam, Li Xiong
PublisherAssociation for Computing Machinery, Inc
Pages385-388
Number of pages4
ISBN (Electronic)9781450380195
DOIs
Publication statusPublished - 3 Nov 2020
Event28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020 - Virtual, Online, United States
Duration: 3 Nov 20206 Nov 2020

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Conference

Conference28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020
Country/TerritoryUnited States
CityVirtual, Online
Period3/11/206/11/20

Keywords / Materials (for Non-textual outputs)

  • Candidate selection
  • Deep learning
  • Fuzzy String Matching
  • Toponym matching

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