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<p>The goal of information extraction is to extract structural knowledge (such as entities, relations and events) from plain and unstructured texts. Information extraction in legal documents has recently gained a lot of attention in the natural language processing (NLP) community due to the high demand for efficient information extraction for legal practitioners and companies. Given that the legal documents are unique and their processing is challenging, there is a pressing need for applications of NLP techniques to tackle these challenges. In this research, we present a survey on the recent advancements in legal information extraction focusing on three tasks: named entity recognition, relationship extraction and event detection. We report language resources and systems in multiple jurisdictions and languages for each task. Based on the thorough review conducted, we identify insights into the techniques employed and promising research directions that merit further exploration in future studies. We maintain a public repository and consistently update related resources at <a href="https://github.com/DamithDR/legalinformationextraction">https://github.com/DamithDR/legalinformationextraction</a>.</p>

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Survey on legal information extraction: current status and open challenges

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月10日

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  • Damith Premasiri,
  • Tharindu Ranasinghe,
  • Ruslan Mitkov,
  • Mo El-Haj,
  • Ingo Frommholz

摘要

The goal of information extraction is to extract structural knowledge (such as entities, relations and events) from plain and unstructured texts. Information extraction in legal documents has recently gained a lot of attention in the natural language processing (NLP) community due to the high demand for efficient information extraction for legal practitioners and companies. Given that the legal documents are unique and their processing is challenging, there is a pressing need for applications of NLP techniques to tackle these challenges. In this research, we present a survey on the recent advancements in legal information extraction focusing on three tasks: named entity recognition, relationship extraction and event detection. We report language resources and systems in multiple jurisdictions and languages for each task. Based on the thorough review conducted, we identify insights into the techniques employed and promising research directions that merit further exploration in future studies. We maintain a public repository and consistently update related resources at https://github.com/DamithDR/legalinformationextraction.