Legal-Domain Named Entity Recognition (NER) Task

A Legal-Domain Named Entity Recognition (NER) Task is a domain-specific legal NLP task.

• Context:
  • It can (typically) involve identifying and extracting named entities from legal texts, such as contracts, statutes, and case law.
  • It can (often) require specialized training datasets annotated with legal entities like parties, dates, amounts, and legal terms.
  • It can improve the efficiency of legal professionals by automating the extraction of key information from large volumes of legal documents.
  • It can range from simple entity recognition tasks in legal texts to complex multi-entity extraction in diverse legal contexts.
  • It can utilize advanced NLP models, such as BERT, GPT-3, and LegalBERT, fine-tuned specifically for legal text processing.
  • ...
• Example(s):
  • a contract analysis task that identifies and extracts parties involved, effective dates, and payment terms.
  • a legal case analysis task that extracts entities such as judge names, case numbers, and decision dates.
  • ...
• Counter-Example(s):
  • General NER Task, which does not focus on the specialized vocabulary and structure of legal documents and may not perform as well in legal contexts.
  • ...
• See: Medical NER.

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References

2024

• (Zin et al., 2024) ⇒ May Myo Zin, Ha Thanh Nguyen, Ken Satoh, and Fumihito Nishino. (2024). “Addressing Annotated Data Scarcity in Legal Information Extraction.” In: New Frontiers in Artificial Intelligence. ISBN:978-981-97-3076-6 doi:10.1007/978-981-97-3076-6_6
  • NOTES:
    • It investigates the use of Generative Pre-trained Transformers (GPT) to address the challenge of annotated data scarcity in legal Named Entity Recognition (NER).
    • It focuses on NER in the context of contractual legal cases, specifically those involving sale and purchase agreements, aiming to identify and extract entities such as SELLER, BUYER, CONTRACT_NAME, PURCHASE_PRODUCT, PURCHASE_PRICE, and PURCHASE_DATE.
    • It explores the effectiveness of GPT models in generating human-like annotated data to overcome the limitations of manual annotation, which is labor-intensive, time-consuming, and expensive.
    • It compares the performance of a BERT-based NER model fine-tuned on GPT-generated data with models trained on human-created data and the direct application of GPT models for zero-shot entity extraction.

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