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Knowledge Discovery from Business Contracts.

机译：从商务合同中发现知识。

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摘要

A contract is a legally binding agreement between participating parties specifying service requirements and expectations, and stakeholder rights and duties. Additionally, a contract provides a framework for resolution in case of breach of its terms. In current practice, contracts are produced as text documents usually drafted by contract lawyers, thus any insights such as service capabilities, business risks, and interaction relations are hidden in unstructured text. We address the challenge of discovering knowledge from thousands of contracts. To this end, we develop a comprehensive approach implemented as a system, Contract Miner, that is capable of extracting essential information from a large contract repository.;First, service exceptions such as product late delivery, payment default, and bankruptcy reveal critical aspects of business service operations. Though rarely studied before in connection with services, exception extraction can help uncover the potential risks an organization is exposed to. Contract Miner takes advantage of a handful of linguistic patterns to harvest service exceptions at the phrase level.;Second, business events form the backbone of business relationships and correspond to essential business processes such as purchase and payment. Business events, e.g., product delivery, bill payment, and bank interest accrual, are inherently temporally constrained. With a hybrid of linguistic patterns, grammar parsing, and classifications, Contract Miner extracts business events and their corresponding temporal constraints. It applies topic modeling to organize the event lexicon into thematic groups.;Third, normative relationships bear one of the most important aspect of contractual relations capturing commitments, authorizations, and prohibitions. Norms are studied intensively in multiagent systems. They yield guidance for implementing software agents as well as a basis for judging whether the parties are complying with the contract. Based on top of the methods for extracting service exceptions, business events and temporal constraints, Contract Miner uses supervised methods to extract normative relationships.;For experimentation, we apply a real-life repository consisting of thousands of contracts drawn from domains such as manufacturing, supply, and licensing. With human annotated data as gold standard, we evaluate the extraction performance in terms of precision, recall, and F-measure for each of the information extraction task. The results show the viability and promise of Contract Miner for extracting information and discovering knowledge from a large number of contracts. Our approach has widespread applications wherever contracts are employed.
机译：合同是参与方之间的具有法律约束力的协议，其中规定了服务要求和期望以及利益相关者的权利和义务。此外，合同提供了一个在违反条款的情况下进行解决的框架。在当前的实践中，合同通常是由合同律师起草的文本文件，因此，任何见解（例如服务能力，业务风险和交互关系）都隐藏在非结构化文本中。我们解决了从数千份合同中发现知识的挑战。为此，我们开发了一种作为合同矿工系统实施的综合方法，该系统能够从大型合同存储库中提取基本信息。首先，服务异常（例如产品逾期交付，违约付款和破产）揭示了合同的关键方面。商业服务运营。尽管很少进行与服务有关的研究，但异常提取可以帮助发现组织可能面临的潜在风险。 Contract Miner利用少数几种语言模式来在短语级别收集服务异常。其次，业务事件构成业务关系的基础，并与诸如购买和付款之类的基本业务流程相对应。商业事件，例如产品交付，账单支付和应计银行利息，在本质上在时间上受到限制。通过混合语言模式，语法分析和分类，Contract Miner提取业务事件及其相应的时间约束。它应用主题建模将事件词典组织为主题组。第三，规范性关系是捕获承诺，授权和禁止的合同关系的最重要方面之一。在多主体系统中对规范进行了深入研究。它们为实施软件代理提供了指导，并为判断双方是否遵守合同提供了依据。在提取服务异常，业务事件和时间约束的方法的基础上，Contract Miner使用监督方法来提取规范关系。为了进行实验，我们应用了一个真实的存储库，该存储库由成千上万的合同组成，这些合同来自诸如制造，供应和许可。以人类注释数据为金标准，我们针对每个信息提取任务的准确性，召回率和F量度评估了提取性能。结果表明，Contract Miner在从大量合同中提取信息和发现知识的可行性和前景。无论采用哪种合同，我们的方法都有广泛的应用。

著录项

  • 作者

    Gao, Xibin.;

  • 作者单位

    North Carolina State University.;

  • 授予单位 North Carolina State University.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 102 p.
  • 总页数 102
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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