Deep Learning for Toponym Resolution

Geocoding Based on Pairs of Toponyms

Bibliographic Data

ID22034929
AuthorsJacques Fize (0000-0003-1783-934X, Centre National de la Recherche Scientifique, corresponding author), Ludovic Moncla (0000-0002-1590-9546, Centre National de la Recherche Scientifique), Bruno Martins (0000-0002-3856-2936, Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento)
Year2021
Volume10
Issue12
Pages818
Publication date2021-12-02
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi10120818
OpenAlexW3216253159
LanguageEN
Citations received6
References cited28

Geocoding aims to assign unambiguous locations (i.e., geographic coordinates) to place names (i.e., toponyms) referenced within documents (e.g., within spreadsheet tables or textual paragraphs). This task comes with multiple challenges, such as dealing with referent ambiguity (multiple places with a same name) or reference database completeness. In this work, we propose a geocoding approach based on modeling pairs of toponyms, which returns latitude-longitude coordinates. One of the input toponyms will be geocoded, and the second one is used as context to reduce ambiguities. The proposed approach is based on a deep neural network that uses Long Short-Term Memory (LSTM) units to produce representations from sequences of character n-grams. To train our model, we use toponym co-occurrences collected from different contexts, namely textual (i.e., co-occurrences of toponyms in Wikipedia articles) and geographical (i.e., inclusion and proximity of places based on Geonames data). Experiments based on multiple geographical areas of interest—France, United States, Great-Britain, Nigeria, Argentina and Japan—were conducted. Results show that models trained with co-occurrence data obtained a higher geocoding accuracy, and that proximity relations in combination with co-occurrences can help to obtain a slightly higher accuracy in geographical areas with fewer places in the data sources

Ambiguity · Archaeology · Cartography · Geocoding · Geographic coordinate system · Geography · Geolocation · Information retrieval · Linguistics · Referent · Toponymy · World Wide Web · Computer Science · Data Management and Algorithms · Geographic Information Systems Studies · Natural Language Processing Techniques

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