AutoEPRS-20: Extracting Business Process Redesign Suggestions from Natural Language Text

Abstract

In this paper, we have defined an NLP task, for the automatic extraction of business process redesign suggestions from natural language text. In particular, we have employed a systematic protocol to define the task, which is composed of three elements and three sub-tasks. The elements are: a) a real-world process model, b) actual feedback in natural language text, and c) three-level classification of the feedback. The task is composed of two binary and one multi-class classification sub-tasks. The evaluation of the AutoEPRS-20 task is performed using six traditional supervised learning techniques. The results show that the third sub-task is more challenging that the two binary sub-tasks.