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The GELATO Dataset for Legislative NER

arXiv 2603.14130 ↗First posted 14 Mar 2026cs.CLcited by 0

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Abstract (arXiv)

This paper introduces GELATO (Government, Executive, Legislative, and Treaty Ontology), a dataset of U.S. House and Senate bills from the 118th Congress annotated using a novel two-level named entity recognition ontology designed for U.S. legislative texts. We fine-tune transformer-based models (BERT, RoBERTa) of different architectures and sizes on this dataset for first-level prediction. We then use LLMs with optimized prompts to complete the second level prediction. The strong performance of RoBERTa and relatively weak performance of BERT models, as well as the application of LLMs as second-level predictors, support future research in legislative NER or downstream tasks using these model combinations as extraction tools.

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References · 15

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  11. 2022Named Entity Recognition in Indian court judgmentscited by 26
  12. 2023A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Taskscited by 25
3 more
  1. 2020A Dataset of German Legal Documents for Named Entity Recognitioncited by 24
  2. 2022E-NER -- An Annotated Named Entity Recognition Corpus of Legal Textcited by 13
  3. 2021SeqScore: Addressing Barriers to Reproducible Named Entity Recognition Evaluationcited by 8

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