Entity Extraction from EU acquis

Jakub Piskorski; Dominik Skotarczak; Océane Abrard

Entity Extraction from EU acquis

Jakub Piskorski, Dominik Skotarczak, Océane Abrard

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Abstract

We present a new annotated corpus for the task of Nested Entity Recognition from legal acts in the EU acquis, the collection of common rights and obligations that constitute the body of EU law and is incorporated into the legal systems of EU Member States. The corpus is subdivided into two subcorpora: GOLD - human annotated (41 documents) and SILVER - created semi-automatically (537 documents). They contain 28.5K and 275K entity mentions labelled with fine-grained entity types using a dedicated taxonomy of 45 fine-grained entity types. We describe the corpus creation process and present corpus statistics. We use knowledge-, transition-, transformer-, and generative AI-based models to provide baselines and benchmark results.

Anthology ID:: 2026.emnlp-main.2491
Volume:: Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:: EMNLP
SIG:: SIGDAT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 56540–56574
Language:
URL:: https://aclanthology.org/2026.emnlp-main.2491/
DOI:
Bibkey:
Cite (ACL):: Jakub Piskorski, Dominik Skotarczak, and Océane Abrard. 2026. Entity Extraction from EU acquis. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 56540–56574, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: Entity Extraction from EU acquis (Piskorski et al., EMNLP 2026)
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PDF:: https://aclanthology.org/2026.emnlp-main.2491.pdf

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@inproceedings{piskorski-etal-2026-entity,
    title = "Entity Extraction from {EU} acquis",
    author = "Piskorski, Jakub  and
      Skotarczak, Dominik  and
      Abrard, Oc{\'e}ane",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.emnlp-main.2491/",
    pages = "56540--56574",
    ISBN = "979-8-89176-459-0",
    abstract = "We present a new annotated corpus for the task of Nested Entity Recognition from legal acts in the EU acquis, the collection of common rights and obligations that constitute the body of EU law and is incorporated into the legal systems of EU Member States. The corpus is subdivided into two subcorpora: GOLD - human annotated (41 documents) and SILVER - created semi-automatically (537 documents). They contain 28.5K and 275K entity mentions labelled with fine-grained entity types using a dedicated taxonomy of 45 fine-grained entity types. We describe the corpus creation process and present corpus statistics. We use knowledge-, transition-, transformer-, and generative AI-based models to provide baselines and benchmark results."
}

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%0 Conference Proceedings
%T Entity Extraction from EU acquis
%A Piskorski, Jakub
%A Skotarczak, Dominik
%A Abrard, Océane
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F piskorski-etal-2026-entity
%X We present a new annotated corpus for the task of Nested Entity Recognition from legal acts in the EU acquis, the collection of common rights and obligations that constitute the body of EU law and is incorporated into the legal systems of EU Member States. The corpus is subdivided into two subcorpora: GOLD - human annotated (41 documents) and SILVER - created semi-automatically (537 documents). They contain 28.5K and 275K entity mentions labelled with fine-grained entity types using a dedicated taxonomy of 45 fine-grained entity types. We describe the corpus creation process and present corpus statistics. We use knowledge-, transition-, transformer-, and generative AI-based models to provide baselines and benchmark results.
%U https://aclanthology.org/2026.emnlp-main.2491/
%P 56540-56574

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Markdown (Informal)

[Entity Extraction from EU acquis](https://aclanthology.org/2026.emnlp-main.2491/) (Piskorski et al., EMNLP 2026)

Entity Extraction from EU acquis (Piskorski et al., EMNLP 2026)

ACL

Jakub Piskorski, Dominik Skotarczak, and Océane Abrard. 2026. Entity Extraction from EU acquis. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 56540–56574, Budapest, Hungary. Association for Computational Linguistics.