Legal NLP Task
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A Legal NLP Task is a domain-specific NLP task for legal documents.
- 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)
- See: LEGAL-BERT, Legal Document Corpus.
References
2023
- (Fei, Shen et al., 2023) ⇒ Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Songyang Zhang, Kai Chen, Zongwen Shen, and Jidong Ge. (2023). “LawBench: Benchmarking Legal Knowledge of Large Language Models.” In: arXiv preprint arXiv:2309.16289. doi:10.48550/arXiv.2309.16289
- QUOTE:
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 |
2023
- (Song et al., 2022) ⇒ Dezhao Song, Sally Gao, Baosheng He, and Frank Schilder. (2022). “On the Effectiveness of Pre-trained Language Models for Legal Natural Language Processing: An Empirical Study.” In: IEEE Access, 10. doi:10.1109/ACCESS.2022.3190408
- QUOTE: ... We present the first comprehensive empirical evaluation of pre-trained language models (PLMs) for legal natural language processing (NLP) in order to examine their effectiveness in this domain. ...
2020
- (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.