LeArNER: Few-shot Legal Argument Named Entity Recognition

Shao Man Lee, Yu Hsiang Tan, Han Ting Yu

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

Our proposed NER model for legal texts, LeArNER, utilizes minimal annotated data for model training to reduce expenses associated with corpus collection and training. We evaluated our model on a dataset of constitutional legal cases from Taiwan, written in traditional Chinese, and achieved an impressive F1 score of 94.88% for 13 entity types. LeArNER's performance was best achieved with a training sample of only 2000 sentences, highlighting its efficiency and potential for further legal NER research.

原文English
主出版物標題19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Proceedings of the Conference
發行者Association for Computing Machinery, Inc
頁面422-426
頁數5
ISBN(電子)9798400701979
DOIs
出版狀態Published - 2023 6月 19
事件19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Braga, Portugal
持續時間: 2023 6月 192023 6月 23

出版系列

名字19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Proceedings of the Conference

Conference

Conference19th International Conference on Artificial Intelligence and Law, ICAIL 2023
國家/地區Portugal
城市Braga
期間23-06-1923-06-23

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 軟體
  • 法律

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