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LLM pipeline extracts structured data from criminal court decisions

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new LLM-based pipeline to extract structured data from criminal court decisions, aiding empirical legal research. The system allows legal experts to refine annotation instructions, which was shown to improve extraction accuracy and annotator agreement for offenses like driving under the influence. The pipeline's flexibility in attribute exploration and value normalization was evaluated on traffic offenses, demonstrating its potential for creating structured datasets from complex legal documents. AI

IMPACT Enables more efficient and accurate legal research by structuring unstructured court data.

RANK_REASON The item is an academic paper detailing a new method for information extraction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM pipeline extracts structured data from criminal court decisions

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The item is an academic paper detailing a new method for information extraction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Tom\'a\v{s} Knap, Ivana Kvapil\'ikov\'a, Jan \v{C}ern\'y, Kl\'ara Bendov\'a, Jarom\'ir \v{S}avelka, Vojt\v{e}ch Pour, Jakub Dr\'apal ·

    Unlocking Criminal Court Decisions: Flexible Information Extraction for Empirical Legal Research

    arXiv:2611.03051v1 Announce Type: new Abstract: Criminal verdicts contain descriptions of criminal behavior that can support quantitative legal research, but structuring them requires attributes and extraction rules that cannot always be fully specified in advance. We present an …

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