Decision Model and Notation (DMN) has become a relevant topic for organizations since it allows users to control their processes and organizational decisions. The increasing use of DMN decision tables to capture critical business knowledge raises the need for supporting analysis tasks such as the extraction of inputs, outputs and their relations from natural language descriptions. In this paper, we create a stepping stone towards implementing a Natural Language Processing framework to model decisions based on the DMN standard. Our proposal contributes to the generation of decision rules and tables from a single sentence analysis. This framework comprises three phases: (1) discourse and semantic analysis, (2) syntactic analysis and (3) decision table construction. To the best of our knowledge, this is the first attempt devoted to automatically discovering decision rules according to the DMN terminology from natural language descriptions. Aiming at assessing the quality of the resultant decision tables, we have conducted a survey involving 16 DMN experts. The results have shown that our framework is able to generate semantically correct tables. It is convenient to mention that our proposal does not aim to replace analysts but support them in creating better models with less effort.

决策模型和表示法（DMN）已成为组织的相关主题，因为它允许用户控制其流程和组织决策。DMN决策表越来越多地用于捕获关键业务知识，这就需要支持分析任务，例如从自然语言描述中提取输入，输出及其关系。在本文中，我们为实现自然语言处理框架以基于DMN标准的决策建模提供了一个垫脚石。我们的建议有助于通过单句分析生成决策规则和表格。该框架包括三个阶段：（1）话语和语义分析；（2）句法分析；（3）决策表构建。据我们所知，这是致力于从自然语言描述中根据DMN术语自动发现决策规则的第一次尝试。为了评估最终决策表的质量，我们进行了一项调查，涉及16位DMN专家。结果表明，我们的框架能够生成语义正确的表。值得一提的是，我们的建议并非旨在取代分析师，而是支持他们以更少的精力创建更好的模型。