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        <title>K-LegalDeID: A Benchmark Dataset and KLUEBERT-CRF for De-identification in Korean Court Judgments</title>
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        <namePart type="given">Wooseok</namePart>
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    <abstract>The Korean legal system mandates public access to court judgments to ensure judicial transparency. However, this requirement conflicts with privacy protection obligations due to the prevalence of Personally Identifiable Information (PII) in legal documents. To address this challenge, we introduce **K-LegalDeID**, a large-scale benchmark dataset and an efficient KLUEBERT-CRF model for de-identification for Korean court judgments. Our primary contribution is a new large-scale benchmark dataset spanning 39 legal domains, with its quality is validated by a high inter-annotator agreement (IAA) with Fleiss’ Kappa of 0.7352. Our results demonstrate that a lightweight KLUEBERT-CRF model, when trained on our dataset, achieves state-of-the-art performance with an entity-level micro F1 score of 0.9923. Our end-to-end framework offers a practical and computationally efficient solution for real-world legal systems.</abstract>
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            <start>2308</start>
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%0 Conference Proceedings
%T K-LegalDeID: A Benchmark Dataset and KLUEBERT-CRF for De-identification in Korean Court Judgments
%A Choi, Wooseok
%A Kim, Hyungbin
%A Chung, Yon Dohn
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F choi-etal-2026-k
%X The Korean legal system mandates public access to court judgments to ensure judicial transparency. However, this requirement conflicts with privacy protection obligations due to the prevalence of Personally Identifiable Information (PII) in legal documents. To address this challenge, we introduce **K-LegalDeID**, a large-scale benchmark dataset and an efficient KLUEBERT-CRF model for de-identification for Korean court judgments. Our primary contribution is a new large-scale benchmark dataset spanning 39 legal domains, with its quality is validated by a high inter-annotator agreement (IAA) with Fleiss’ Kappa of 0.7352. Our results demonstrate that a lightweight KLUEBERT-CRF model, when trained on our dataset, achieves state-of-the-art performance with an entity-level micro F1 score of 0.9923. Our end-to-end framework offers a practical and computationally efficient solution for real-world legal systems.
%U https://aclanthology.org/2026.eacl-long.103/
%P 2308-2325

Markdown (Informal)

[K-LegalDeID: A Benchmark Dataset and KLUEBERT-CRF for De-identification in Korean Court Judgments](https://aclanthology.org/2026.eacl-long.103/) (Choi et al., EACL 2026)

• K-LegalDeID: A Benchmark Dataset and KLUEBERT-CRF for De-identification in Korean Court Judgments (Choi et al., EACL 2026)

ACL

• Wooseok Choi, Hyungbin Kim, and Yon Dohn Chung. 2026. K-LegalDeID: A Benchmark Dataset and KLUEBERT-CRF for De-identification in Korean Court Judgments. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2308–2325, Rabat, Morocco. Association for Computational Linguistics.