Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better results than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of $0.93$. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low.

翻译：从文本描述中提取工作流网可用于简化指南或形式化业务流程、算法等正式过程的文本描述。然而，手动提取流程需要领域知识且耗费精力。尽管自动流程模型提取具有吸引力，但为文本标注形式化流程模型的成本高昂，因此基于机器学习的提取方法较少。基于规则的方法则需要领域特异性才能有效工作，且难以区分文本描述中的相关与无关信息。本文提出了GUIDO——一种用于流程模型提取的混合方法：首先利用基于BERT的句子分类器判断句子与流程模型的相关性；随后采用依存句法分析从相关句子中提取流程模型。该方法显著优于纯规则方法，平均行为相似度得分达0.93。同时，与纯机器学习方法相比，其标注成本仍保持较低水平。