@inproceedings{moncla-etal-2026-edda,
title = "{EDDA}-Coordinata: An Annotated Dataset of Historical Geographic Coordinates",
author = "Moncla, Ludovic and
Nugues, Pierre and
Joliveau, Thierry and
McDonough, Katherine",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.493/",
doi = "10.63317/5guc63fgjocp",
pages = "6224--6234",
abstract = "This paper introduces a dataset of enriched geographic coordinates retrieved from Diderot and d{'}Alembert{'}s eighteenth-century Encyclop{\'e}die. Automatically recovering geographic coordinates from historical texts is a complex task, as they are expressed in a variety of ways and with varying levels of precision. To improve retrieval of coordinates from similar digitized early modern texts, we have created a gold standard dataset, trained models, published the resulting inferred and normalized coordinate data, and experimented applying these models to new texts. From 74,000 total articles in each of the digitized versions of the Encyclop{\'e}die from ARTFL and ENCCRE, we examined 15,278 geographical entries, manually identifying 4,798 containing coordinates, and 10,480 with descriptive but non-numerical references. Leveraging our gold standard annotations, we trained transformer-based models to retrieve and normalize coordinates. The pipeline presented here combines a classifier to identify coordinate-bearing entries and a second model for retrieval, tested across encoder{--}decoder and decoder architectures. Cross-validation yielded an 86{\%} EM score. On an out-of-domain eighteenth-century Tr{\'e}voux dictionary (also in French), our fine-tuned model had an 61{\%} EM score, while for the nineteenth-century, 7th edition of the Encyclop{\ae}dia Britannica in English, the EM was 77{\%}. These findings highlight the gold standard dataset{'}s usefulness as training data, and our two-step method{'}s cross-lingual, cross-domain generalizability."
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<abstract>This paper introduces a dataset of enriched geographic coordinates retrieved from Diderot and d’Alembert’s eighteenth-century Encyclopédie. Automatically recovering geographic coordinates from historical texts is a complex task, as they are expressed in a variety of ways and with varying levels of precision. To improve retrieval of coordinates from similar digitized early modern texts, we have created a gold standard dataset, trained models, published the resulting inferred and normalized coordinate data, and experimented applying these models to new texts. From 74,000 total articles in each of the digitized versions of the Encyclopédie from ARTFL and ENCCRE, we examined 15,278 geographical entries, manually identifying 4,798 containing coordinates, and 10,480 with descriptive but non-numerical references. Leveraging our gold standard annotations, we trained transformer-based models to retrieve and normalize coordinates. The pipeline presented here combines a classifier to identify coordinate-bearing entries and a second model for retrieval, tested across encoder–decoder and decoder architectures. Cross-validation yielded an 86% EM score. On an out-of-domain eighteenth-century Trévoux dictionary (also in French), our fine-tuned model had an 61% EM score, while for the nineteenth-century, 7th edition of the Encyclopædia Britannica in English, the EM was 77%. These findings highlight the gold standard dataset’s usefulness as training data, and our two-step method’s cross-lingual, cross-domain generalizability.</abstract>
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%0 Conference Proceedings
%T EDDA-Coordinata: An Annotated Dataset of Historical Geographic Coordinates
%A Moncla, Ludovic
%A Nugues, Pierre
%A Joliveau, Thierry
%A McDonough, Katherine
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F moncla-etal-2026-edda
%X This paper introduces a dataset of enriched geographic coordinates retrieved from Diderot and d’Alembert’s eighteenth-century Encyclopédie. Automatically recovering geographic coordinates from historical texts is a complex task, as they are expressed in a variety of ways and with varying levels of precision. To improve retrieval of coordinates from similar digitized early modern texts, we have created a gold standard dataset, trained models, published the resulting inferred and normalized coordinate data, and experimented applying these models to new texts. From 74,000 total articles in each of the digitized versions of the Encyclopédie from ARTFL and ENCCRE, we examined 15,278 geographical entries, manually identifying 4,798 containing coordinates, and 10,480 with descriptive but non-numerical references. Leveraging our gold standard annotations, we trained transformer-based models to retrieve and normalize coordinates. The pipeline presented here combines a classifier to identify coordinate-bearing entries and a second model for retrieval, tested across encoder–decoder and decoder architectures. Cross-validation yielded an 86% EM score. On an out-of-domain eighteenth-century Trévoux dictionary (also in French), our fine-tuned model had an 61% EM score, while for the nineteenth-century, 7th edition of the Encyclopædia Britannica in English, the EM was 77%. These findings highlight the gold standard dataset’s usefulness as training data, and our two-step method’s cross-lingual, cross-domain generalizability.
%R 10.63317/5guc63fgjocp
%U https://aclanthology.org/2026.lrec-1.493/
%U https://doi.org/10.63317/5guc63fgjocp
%P 6224-6234
Markdown (Informal)
[EDDA-Coordinata: An Annotated Dataset of Historical Geographic Coordinates](https://aclanthology.org/2026.lrec-1.493/) (Moncla et al., LREC 2026)
ACL