The automatic transformation of verbose, natural language descriptions into structured process models remains a challenge of significant complexity - This paper introduces a contemporary solution, where central to our approach, is the use of dependency parsing and Named Entity Recognition (NER) for extracting key elements from textual descriptions. Additionally, we utilize Subject-Verb-Object (SVO) constructs for identifying action relationships and integrate semantic analysis tools, including WordNet, for enriched contextual understanding. A novel aspect of our system is the application of neural coreference resolution, integrated with the SpaCy framework, enhancing the precision of entity linkage and anaphoric references. Furthermore, the system adeptly handles data transformation and visualization, converting extracted information into BPMN (Business Process Model and Notation) diagrams. This methodology not only streamlines the process of capturing and representing business workflows but also significantly reduces the manual effort and potential for error inherent in traditional modeling approaches.

翻译：冗长自然语言描述到结构化过程模型的自动转换仍是一项复杂度极高的挑战。本文提出了一种当代解决方案，其核心方法采用依存句法分析和命名实体识别（NER）来抽取文本描述中的关键元素。此外，我们利用主谓宾（SVO）结构识别行动关系，并集成包括WordNet在内的语义分析工具以增强上下文理解。系统的一个创新点在于应用了基于SpaCy框架的神经共指消解技术，显著提升了实体链接与回指消解的精确度。同时，本系统能高效处理数据转换与可视化，将抽取的信息自动转化为BPMN（业务流程模型与符号）图示。该方法不仅简化了业务流程的捕获与表示过程，还显著降低了传统建模方法中固有的人工操作量和潜在错误率。