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    <abstract>Legal citation detection in court judgments underpins reliable precedent mapping, citation analytics, and document retrieval. Extracting references to legislation and case law in the United Kingdom is especially challenging: citation styles have evolved over centuries, and judgments routinely cite foreign or historical authorities. We conduct the first systematic comparison of three modelling paradigms on this task using the Cambridge Law Corpus: (i) rule-based regular expressions; (ii) transformer-based encoders (BERT, RoBERTa, LEGAL-BERT, ModernBERT); and (iii) large language models (GPT-4.1). We produced a gold-standard high-quality corpus of 190 court judgments containing 45,179 fine-grained annotations for UK and non-UK legislation and case references. ModernBERT achieves a macro-averaged F1 of 93.3%, only marginally ahead of the other encoder-only models, yet significantly outperforming the strongest regular-expression baseline (35.42% F1) and GPT-4.1 (76.57% F1).</abstract>
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%0 Conference Proceedings
%T Detecting Legal Citations in United Kingdom Court Judgments
%A Sargeant, Holli
%A Östling, Andreas
%A Magnusson, Måns
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F sargeant-etal-2025-detecting
%X Legal citation detection in court judgments underpins reliable precedent mapping, citation analytics, and document retrieval. Extracting references to legislation and case law in the United Kingdom is especially challenging: citation styles have evolved over centuries, and judgments routinely cite foreign or historical authorities. We conduct the first systematic comparison of three modelling paradigms on this task using the Cambridge Law Corpus: (i) rule-based regular expressions; (ii) transformer-based encoders (BERT, RoBERTa, LEGAL-BERT, ModernBERT); and (iii) large language models (GPT-4.1). We produced a gold-standard high-quality corpus of 190 court judgments containing 45,179 fine-grained annotations for UK and non-UK legislation and case references. ModernBERT achieves a macro-averaged F1 of 93.3%, only marginally ahead of the other encoder-only models, yet significantly outperforming the strongest regular-expression baseline (35.42% F1) and GPT-4.1 (76.57% F1).
%U https://aclanthology.org/2025.emnlp-main.1361/
%P 26798-26824

Markdown (Informal)

[Detecting Legal Citations in United Kingdom Court Judgments](https://aclanthology.org/2025.emnlp-main.1361/) (Sargeant et al., EMNLP 2025)

• Detecting Legal Citations in United Kingdom Court Judgments (Sargeant et al., EMNLP 2025)

ACL

• Holli Sargeant, Andreas Östling, and Måns Magnusson. 2025. Detecting Legal Citations in United Kingdom Court Judgments. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 26798–26824, Suzhou, China. Association for Computational Linguistics.