从对话到图表：用于加速业务流程原型设计的自然语言任务与关系抽取
From Dialogue to Diagram: Task and Relationship Extraction from Natural Language for Accelerated Business Process Prototyping
AI总结：
针对自然语言转结构化流程模型的难题，提出融合依存分析、NER、SVO结构、语义工具及神经共指消解的方案，可将文本转为BPMN图表，减少人工成本与误差。
摘要：
将冗长的自然语言描述自动转换为结构化流程模型仍是一项复杂度极高的挑战。本文提出了一套现代化解决方案，其核心方法是利用依存句法分析和命名实体识别（NER）从文本描述中抽取关键要素。此外，我们使用主谓宾（SVO）结构识别动作关系，并整合包括WordNet在内的语义分析工具以丰富语境理解。本系统的一个创新点是应用集成在SpaCy框架中的神经共指消解，提升了实体链接和回指参照的精度。此外，该系统可熟练处理数据转换与可视化，将抽取的信息转换为BPMN（业务流程模型与符号）图表。该方法不仅简化了业务工作流的捕获与表示流程，还显著降低了传统建模方法中固有的人工工作量与潜在误差。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
