Litigation among stakeholders in construction projects has a significantly negative impact on successful project completion and overall performance. Prompt decision-making in relation to litigation is crucial, but the manual review of extensive document sets is time-consuming. In this paper, the natural language processing (NLP) technique was applied to litigation data to develop a model for case summarization and winner prediction. By automatically summarizing the data and predicting litigation outcomes, the proposed model aids practitioners in making timely decisions and enhances document management during disputes. This paper contributes to existing knowledge in two ways. Firstly, the model aids practitioners in making timely decisions about proceeding with litigation. Secondly, unlike previous studies that manually processed raw data such as contracts and specifications, this study utilized NLP to process raw litigation case data automatically. As big data becomes increasingly common, the methodology employed in this study holds academic significance.

中文翻译：

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用于分析建筑诉讼先例以支持决策的 AI 模型


建筑项目利益相关者之间的诉讼对项目成功完成和整体绩效有重大负面影响。及时做出与诉讼相关的决策至关重要，但手动审查大量文档集非常耗时。在本文中，将自然语言处理 （NLP） 技术应用于诉讼数据，以开发案例摘要和获胜者预测模型。通过自动汇总数据和预测诉讼结果，所提出的模型可帮助从业者及时做出决策，并增强争议期间的文档管理。本文以两种方式为现有知识做出贡献。首先，该模型帮助从业者及时做出有关诉讼继续的决定。其次，与之前手动处理合同和规范等原始数据的研究不同，本研究利用 NLP 自动处理原始诉讼案件数据。随着大数据变得越来越普遍，本研究中采用的方法具有学术意义。