Paper Info
Title
AutoEPRS-20: extracting business process redesign suggestions from natural language text
Abstract
ABSTRACTIn 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.
Year
DOI
Venue
2020
10.1145/3417113.3423374
ASE
Keywords
DocType
ISSN
Automated software engineering, business process management, business process redesign, Natural Language Processing
Conference
2151-0830
ISBN
Citations 
PageRank 
978-1-7281-7295-8
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Amina Mustansir101.01
Khurram Shahzad216525.77
Muhammad Kamran Malik3152.63