Deep Legal Contract Analysis Task
A Deep Legal Contract Analysis Task is a legal contract analysis task that is a document understanding task for legal contracts.
- AKA: Contract Understanding.
- Context:
- It can range from being a Human-Performed Deep Legal Contract Analysis Task to being a AI-Performed Deep Legal Contract Analysis Task (AI-powered textual analysis).
- ...
- Example(s):
- Counter-Example(s):
- See: Legal Contract NLP, Contract Management Software, LegalTech.
References
2023a
- (ChatGPT, 2023a) ⇒ "What is a "Deep Legal Contract Analysis Task"? Summarize in 2-3 paragraphs." prompt. ChatGPT, September 25 Version, OpenAI, 8 Oct. 2023. https://chat.openai.com.
- QUOTE: A deep legal contract analysis task refers to the process of using advanced technologies, often powered by artificial intelligence (AI) and natural language processing (NLP), to thoroughly examine and understand the contents of a legal contract. This task goes beyond surface-level review and involves a comprehensive analysis of the contract's language, clauses, terms, and implications to extract meaningful insights and ensure compliance with legal standards.
In a deep legal contract analysis, AI systems are trained to identify and interpret complex legal language, detect potential risks, inconsistencies, and ambiguities within the contract, and compare it against relevant legal precedents and regulations. This analysis aims to provide a detailed assessment of the contract's legal and financial implications, helping legal professionals, businesses, and individuals make informed decisions. Additionally, it can streamline the contract review process, saving time and reducing the risk of oversight or errors in legal agreements.
Deep legal contract analysis tasks can be performed using AI-powered contract review platforms, which use machine learning algorithms to analyze contracts quickly and accurately. These platforms can provide summaries, risk assessments, and recommendations for revisions, making them valuable tools for legal professionals and organizations seeking to enhance their contract management and compliance efforts.
- QUOTE: A deep legal contract analysis task refers to the process of using advanced technologies, often powered by artificial intelligence (AI) and natural language processing (NLP), to thoroughly examine and understand the contents of a legal contract. This task goes beyond surface-level review and involves a comprehensive analysis of the contract's language, clauses, terms, and implications to extract meaningful insights and ensure compliance with legal standards.
2023b
- (Martinez, 2023) ⇒ Juan Martinez. (2023). “Legal Contract Understanding - with Legal NLP library." John Snow Labs webinar
2020
- (Juro, 2020) ⇒ https://juro.com/learn/contract-elements-requirements
- QUOTE: The elements of a legal contract vary around the world according to jurisdiction. However, there are some common elements that persist across different legal systems. ...
- The contract law of England and Wales is historically influential, and many of its principles have been incorporated or reflected across the English-speaking world - particularly in Commonwealth countries like Australia and Canada.
- The elements of a contract in the US are similar to that of the UK, with slight variations:
- Offer and acceptance
- Awareness
- Consideration
- Capacity
- Legality
- All these elements must be present for a contract to be binding, and if just one of them is missing, the agreement may not be legally enforceable. Read more about the difference between a contract and an agreement.
2019
- (Elwany et al., 2019) ⇒ Emad Elwany, Dave Moore, and Gaurav Oberoi. (2019). “BERT Goes to Law School: Quantifying the Competitive Advantage of Access to Large Legal Corpora in Contract Understanding.” arXiv preprint arXiv:1911.00473. DOI:10.48550/arXiv.1911.00473
- ABSTRACT: Fine-tuning language models, such as BERT, on domain specific corpora has proven to be valuable in domains like scientific papers and biomedical text. In this paper, we show that fine-tuning BERT on legal documents similarly provides valuable improvements on NLP tasks in the legal domain. Demonstrating this outcome is significant for analyzing commercial agreements, because obtaining large legal corpora is challenging due to their confidential nature. As such, we show that having access to large legal corpora is a competitive advantage for commercial applications, and academic research on analyzing contracts.