@inproceedings{beersmans-etal-2026-claudius,
title = "{I}, {RE}:Claudius 256: Towards Linking Classical {L}atin Person Mentions to a Domain-specific Knowledge Base",
author = "Beersmans, Marijke and
de Graaf, Evelien and
Nijs, Julie and
Boano, Valeria Irene and
Keersmaekers, Alek and
Depauw, Mark and
Van de Cruys, Tim and
Fantoli, Margherita",
editor = "Sprugnoli, Rachele and
Passarotti, Marco",
booktitle = "Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages ({LT}4{HALA} 2026) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.lt4hala-1.14/",
doi = "10.63317/3db3992kjxgv",
pages = "152--162",
abstract = "This paper considers Named Entity Linking for person mentions from classical Latin texts to a domain-specific, German language knowledge base, namely Paulys Realencyclop{\ensuremath{\Sigma}}die. Following a methodology similar to (anonymous{\_}reference), we train a transformer-based, retrieval and ranking model (BLINK) first on a general, Wikipedia-derived dataset and subsequently on a more specific dataset, gathered from various sources, linking to our target knowledge base. Results show that while BLINK performs well on mention-entity pairs linked to entities seen during training, it performs significantly worse on mention-entity pairs linking to unseen entities. We provide a detailed error analysis, propose possible exploitation strategies for a human-in-the-loop approach, and identify directions for future improvement."
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<abstract>This paper considers Named Entity Linking for person mentions from classical Latin texts to a domain-specific, German language knowledge base, namely Paulys Realencyclop\ensuremathΣdie. Following a methodology similar to (anonymous_reference), we train a transformer-based, retrieval and ranking model (BLINK) first on a general, Wikipedia-derived dataset and subsequently on a more specific dataset, gathered from various sources, linking to our target knowledge base. Results show that while BLINK performs well on mention-entity pairs linked to entities seen during training, it performs significantly worse on mention-entity pairs linking to unseen entities. We provide a detailed error analysis, propose possible exploitation strategies for a human-in-the-loop approach, and identify directions for future improvement.</abstract>
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%0 Conference Proceedings
%T I, RE:Claudius 256: Towards Linking Classical Latin Person Mentions to a Domain-specific Knowledge Base
%A Beersmans, Marijke
%A de Graaf, Evelien
%A Nijs, Julie
%A Boano, Valeria Irene
%A Keersmaekers, Alek
%A Depauw, Mark
%A Van de Cruys, Tim
%A Fantoli, Margherita
%Y Sprugnoli, Rachele
%Y Passarotti, Marco
%S Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F beersmans-etal-2026-claudius
%X This paper considers Named Entity Linking for person mentions from classical Latin texts to a domain-specific, German language knowledge base, namely Paulys Realencyclop\ensuremathΣdie. Following a methodology similar to (anonymous_reference), we train a transformer-based, retrieval and ranking model (BLINK) first on a general, Wikipedia-derived dataset and subsequently on a more specific dataset, gathered from various sources, linking to our target knowledge base. Results show that while BLINK performs well on mention-entity pairs linked to entities seen during training, it performs significantly worse on mention-entity pairs linking to unseen entities. We provide a detailed error analysis, propose possible exploitation strategies for a human-in-the-loop approach, and identify directions for future improvement.
%R 10.63317/3db3992kjxgv
%U https://aclanthology.org/2026.lt4hala-1.14/
%U https://doi.org/10.63317/3db3992kjxgv
%P 152-162
Markdown (Informal)
[I, RE:Claudius 256: Towards Linking Classical Latin Person Mentions to a Domain-specific Knowledge Base](https://aclanthology.org/2026.lt4hala-1.14/) (Beersmans et al., LT4HALA 2026)
ACL
- Marijke Beersmans, Evelien de Graaf, Julie Nijs, Valeria Irene Boano, Alek Keersmaekers, Mark Depauw, Tim Van de Cruys, and Margherita Fantoli. 2026. I, RE:Claudius 256: Towards Linking Classical Latin Person Mentions to a Domain-specific Knowledge Base. In Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026, pages 152–162, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).