一种基于数据分解的层次分类方法,用于对合同义务进行多标签分类,用于其治理
Amrita Singh1, Preethu Rose Anish2, Aparna Verma3
1Data and Decision Science (DDS) Department, Tata Consultancy Services (TCS) Research, Pune, Maharashtra, India. s.amrita3@tcs.com.
本研究引入了一种用于自动提取和分类软件工程 (SE) 合同中的义务的新方法,从而改善合同治理. 与现有模型相比,这种方法将准确性提高了7-8%.
科学领域:
- 信息技术 信息技术 信息技术
- 软件工程 软件工程 软件工程
- 合同法 合同法 合同法 合同法
背景情况:
- 有效的合同治理对于管理众多供应商协议的IT外包组织至关重要.
- 手动审查大量合同文本是耗时且容易出现错误的.
- 准确识别和分类合同义务对于分配所有权和责任至关重要.
研究的目的:
- 开发一个自动化系统来提取和分类软件工程 (SE) 合同中的义务.
- 通过促进对合同义务的所有权分配和问责制来加强合同治理.
- 提出一种基于数据分解的新型层次分类方法,用于对合同义务的多标签分类.
主要方法:
- 提出了一种基于数据分解的新层次分类方法.
- 实验涉及从50个SE合同中对超过55,000个陈述进行细粒度的自动分类.
- 该方法与包括BERT,RoBERTa和GPT-2在内的最先进的基线进行了评估.
主要成果:
- 与基线模型相比,拟议的方法在分类准确度上取得了7-8%的改善.
- 成功地将超过55,000个陈述分为152个治理特定类别.
- 证明了对多标签义务分类的层次分类的有效性.
结论:
- 开发的自动化方法显著提高了SE合同中义务提取和分类的准确性.
- 这一进步为IT外包中合同治理提供了更有效,更可靠的方法.
- 新的层次分类技术为复杂的多标签分类任务提供了强大的解决方案.
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