From Dialogue to Diagram: Task and Relationship Extraction from Natural
  Language for Accelerated Business Process Prototyping
Sara Qayyum Muhammad Moiz Asghar Muhammad Fouzan Yaseen
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.