Contract-Related Classification Task

A Contract-Related Classification Task is a legal document classification task whose output range is contract-related categories.

• Context:
  • It can range from Simple Contract Categorizations, such as distinguishing between sales contracts and service contracts, to more complex tasks, such as identifying specific sub-types within broader categories.
  • ...
• Example(s):
  • a Contract Type Classification Task, such as:
    • a Commercial Contract Categorization Task, where contracts are sorted into categories like "Sales Agreements," "Service Contracts," and "Partnership Agreements.", "Non-Disclosure Agreements (NDAs)," "Master Service Agreements (MSAs)," and "Lease Agreements"
  • a Contract Obligation Classification Task, classifying specific obligations like payment obligations, performance obligations, and confidentiality obligations.
  • a Contract Party Classification Task, categorizing the parties involved, such as buyer, seller, service provider, and client.
  • a Contract Date Classification Task, identifying and categorizing key dates like effective date, termination date, and renewal date.
  • a Contract Amendment Classification Task, classifying amendments or modifications such as scope change, price adjustment, and term extension.
  • a Contract Term Classification Task, categorizing terms and conditions like payment terms, delivery terms, liability terms, and termination terms.
  • a Contract Sentence Classification Task, categorizing sentences within a contract based on their function and legal implications.
  • ..
  • One based on ContractNLI.
  • ...
• Counter-Example(s):
  • A Contract Article Classification Task (for contract articles).
  • A Legal Case Classification Task, this involves categorizing legal cases or judgments rather than contract types.​
• See: Document Classification, Text Classification Task, Contract Type, Legal Text Analysis, Natural Language Processing, Sentiment Analysis, Pattern Recognition.

---

---

References

2024

• (Zhao, Yang, Gao, 2024) ⇒ Yu Zhao, Shiqi Yang, and Haoxiang Gao. (2024). “Utilizing Large Language Models to Analyze Common Law Contract Formation.” OSF Preprints.
  • The primary NLP task in the paper is the binary classification of the presence or absence of each contract formation element (offer, acceptance, consideration, and defenses) within fictional situations used for educational purposes to demonstrate the principles of contract formation

2024

• (ChatGPT-OpenAI, 2024) ⇒ Prompt: Summarize the concept Contract Classification Task in 1- 2 paragraphs. ChatGPT (model:gpt-4o). Retrieved: 2024-06-10.
  • QUOTE: The "Contract Classification Task" involves categorizing legal contracts into predefined classes or categories based on their content and characteristics. This task is crucial for legal professionals, businesses, and organizations to efficiently manage, review, and analyze large volumes of contract documents. By classifying contracts, one can quickly identify the type of agreement, such as non-disclosure agreements, service agreements, employment contracts, or lease agreements, which facilitates better organization, risk assessment, and compliance management.

    This task often leverages natural language processing (NLP) and machine learning techniques to automate the classification process. By training algorithms on labeled datasets with examples of various contract types, these systems can learn to recognize patterns and semantic features that distinguish different contracts. This automation not only speeds up the classification process but also improves accuracy and reduces the potential for human error, thereby streamlining legal workflows and enhancing decision-making capabilities.

2022

• (Shi et al., 2022) ⇒ Chaochen Shi, Yong Xiang, Robin Ram Mohan Doss, Jiangshan Yu, Keshav Sood, and Longxiang Gao (2022, March). A Bytecode-Based Approach For Smart Contract Classification. In: 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER).
  • QUOTE: The automatic classification of smart contracts can provide blockchain users with keyword-based contract searching and helps to manage smart contracts effectively. Current research on smart contract classification focuses on Natural Language Processing (NLP) solutions which are based on contract source code. However, more than 94% of smart contracts are not open-source, so the application scenarios of NLP methods are very limited. Meanwhile, NLP models are vulnerable to adversarial attacks. This paper proposes a classification model based on features from contract bytecode instead of source code to solve these problems.

---