Legal NLP Task

Context:
- ...
Example(s):
- Legal Document Information Extraction that involves extracting specific information like party names, dates, monetary amounts, and obligations from legal texts.
- Contract NLP, such as: clause-to-provision transformation, which transforms clauses in contracts into distinct, actionable provisions.
- Predictive Legal Coding in legal cases to prioritize documents by their likelihood of relevance to reduce manual review costs.
- Legal Sentiment Analysis to gauge public or specific group sentiments towards legal decisions or laws.
- Legal Topic Modeling to automatically identify topics from large volumes of legal documents.
- Legal Compliance Monitoring to ensure that legal documents or company practices adhere to regulations and laws.
- Automated Legal Advice Generation to provide automated responses to common legal inquiries using NLP techniques.
- Legal Document Summarization which involves condensing case files into shorter, comprehensible summaries.
- Legal Document Translation which ...
- Jury Decision Prediction to analyze previous case data and predict outcomes based on similar historical contexts.
- ...
Counter-Example(s)
- Medical NLP.
- Biomedical NLP.
See: LEGAL-BERT, Legal Document Corpus.

References

Cognitive Level	ID	Task	Data Source	Metric	Type
Legal Knowledge Memorization	1-1	Article Recitation	FLK	Rouge-L	Generation
	1-2	Knowledge Question Answering	JEC_QA	Accuracy	SLC

	2-1	Document Proofreading	CAIL2022	F0.5	Generation
	2-2	Dispute Focus Identification	LAIC2021	F1	MLC
	2-3	Marital Disputes Identification	AIStudio	F1	MLC
Legal Knowledge Understanding	2-4	Issue Topic Identification Reading Comprehension	CrimeKgAssitant CAIL2019	Accuracy rc-F1	SLC
	2-5	Reading Comprehension	CAIL2019	rc-F1	Extraction
	2-6	Named-Entity Recognition	CAIL2022	soft-F1	Extraction
	2-7	Opinion Summarization	CAIL2021	Rouge-L	Generation
	2-8	Argument Mining	CAIL2022	Accuracy	SLC
	2-9	Event Detection	LEVEN	F1	MLC
	2-10	Trigger Word Extraction	LEVEN	soft-F1	Extraction

	3-1	Fact-based Article Prediction	CAIL2018	F1	MLC
	3-2	Scene-based Article Prediction	LawGPT	Rouge-L	Generation
	3-3	Charge Prediction	CAIL2018	F1	MLC
Legal Knowledge Applying	3-4	Prison Term Prediction w.o. Article	CAIL2018	nLog-distance	Regression
	3-5	Prison Term Prediction w. Article	CAIL2018	nLog-distance	Regression
	3-6	Case Analysis	JEC_QA	Accuracy	SLC
	3-7	Criminal Damages Calculation	LAIC2021	Accuracy	Regression
	3-8	Consultation	hualv.com	Rouge-L	Generation

(Chalkidis et al., 2020) ⇒ Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, Nikolaos Aletras, and Ion Androutsopoulos. (2020). “LEGAL-BERT: The Muppets Straight Out of Law School.” arXiv preprint arXiv:2010.02559 DOI:10.48550/arXiv.2010.02559.
- ABSTRACT: BERT has achieved impressive performance in several NLP tasks. However, there has been limited investigation on its adaptation guidelines in specialised domains. Here we focus on the legal domain, where we explore several approaches for applying BERT models to downstream legal tasks, evaluating on multiple datasets. Our findings indicate that the previous guidelines for pre-training and fine-tuning, often blindly followed, do not always generalize well in the legal domain. Thus we propose a systematic investigation of the available strategies when applying BERT in specialised domains. These are: (a) use the original BERT out of the box, (b) adapt BERT by additional pre-training on domain-specific corpora, and (c) pre-train BERT from scratch on domain-specific corpora. We also propose a broader hyper-parameter search space when fine-tuning for downstream tasks and we release LEGAL-BERT, a family of BERT models intended to assist legal NLP research, computational law, and legal technology applications.