Legal-Domain Natural Language Processing (NLP) Benchmark Task
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A Legal-Domain Natural Language Processing (NLP) Benchmark Task is an domain-specific NLP benchmark task for a legal text analysis task.
- Context:
- It can range from being a Low-Level Legal NLP Task (of legal named entity recognition) to being a High-Level Legal NLP Task (of legal reasoning and inference).
- It can range from being a Monolingual Legal NLP Benchmark (of single jurisdiction legal texts) to being a Multilingual Legal NLP Benchmark (of cross-jurisdictional legal texts).
- It can range from being a Narrow Legal NLP Benchmark (of specific legal domain) to being a Broad Legal NLP Benchmark (of diverse legal domains).
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- It can include tasks like legal document summarization, legal information extraction, and legal question answering.
- It can include datasets derived from legal documents, court rulings, or legal literature.
- It can play a critical role in advancing the field of legal tech and AI in law.
- It can help in evaluating legal language models for tasks such as contract analysis, case law research, and regulatory compliance.
- It can be used to assess the ethical considerations and bias mitigation in legal AI systems.
- It can contribute to the development of explainable AI in the legal domain, enhancing transparency and trust.
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- Example(s):
- LEXTREME: A comprehensive multi-lingual and multi-task benchmark for the legal domain.
- LegalBench: A benchmark suite for evaluating legal language models on various tasks.
- ContractNLI: A dataset for contract understanding and clause extraction.
- CASEHOLD: A benchmark for legal case retrieval and entailment.
- Legal-BERT: A BERT model fine-tuned on legal corpora, with associated benchmark tasks.
- LEDGAR: A large-scale multilabel corpus of legal provisions for contract understanding.
- EUR-LEX: A multi-label text classification dataset of EU laws.
- HOLJ: A corpus of legal judgments from the UK House of Lords.
- CaseHOLD: A dataset for case law analysis and holdings extraction.
- JEC-QA: A dataset for legal question answering in Chinese.
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- Counter-Example(s):
- Clinical Trial Dataset: A dataset for medical research, not legal analysis.
- ImageNet Dataset: A large-scale image recognition dataset, unrelated to legal text.
- Question-Answer Dataset: A general dataset not specific to legal domain.
- Reading Comprehension Dataset: A general language understanding dataset.
- General Language Understanding Evaluation (GLUE): A benchmark designed for general language understanding.
- Stanford Question Answering Dataset (SQuAD): A benchmark for general question answering tasks.
- TREC Legal Track: A track within TREC focusing on e-discovery rather than a broad range of legal NLP tasks.
- PubMed NLP Dataset: A dataset for biomedical text analysis, not legal text.
- Financial NLP Benchmark: A benchmark for financial text analysis, distinct from legal domain.
- Social Media Sentiment Analysis Dataset: A dataset for analyzing social media content, not legal documents.
- See: Biomedical NLP Benchmark, General NLP Benchmark, Natural Language Understanding, Legal AI Ethics, Legal Text Mining, Computational Law, Legal Expert Systems, Legal Information Retrieval, Legal Knowledge Representation, Legal Reasoning Systems.
References
2023
- (Greco & Tagarelli, 2023) ⇒ Candida M Greco, and Andrea Tagarelli. (2023). “Bringing Order Into the Realm of Transformer-based Language Models for Artificial Intelligence and Law.” In: Artif. Intell. Law Journal. doi:10.48550/arXiv.2308.05502