TY  - CHAP
A1  - Freyer, Nils
A1  - Thewes, Dustin
A1  - Meinecke, Matthias
ED  - Gusikhin, Oleg
ED  - Hammoudi, Slimane
ED  - Cuzzocrea, Alfredo
T1  - GUIDO: a hybrid approach to guideline discovery & ordering from natural language texts
T2  - Proceedings of the 12th International Conference on Data Science, Technology and Applications DATA - Volume 1
N2  - Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low.
KW  - Natural Language Processing
KW  - Text Mining
KW  - Process Model Extraction
KW  - Business Process Intelligence
Y1  - 2023
SN  - 978-989-758-664-4
U6  - http://dx.doi.org/10.5220/0012084400003541
SN  - 2184-285X
N1  - Proceedings of the 12th International Conference on Data Science, Technology and Applications, July 11-13, 2023, in Rome, Italy.
SP  - 335
EP  - 342
ER  -