From Dialogue to Diagram: Task and Relationship Extraction from Natural Language for Accelerated Business Process Prototyping
This addresses the problem of inefficient business process prototyping for professionals, though it appears incremental as it builds on existing NLP techniques.
The paper tackles the challenge of automatically transforming natural language descriptions into structured business process models by using dependency parsing, NER, SVO constructs, semantic analysis, and neural coreference resolution, resulting in a system that reduces manual effort and errors in workflow modeling.
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.