GUIDO: A Hybrid Approach to Guideline Discovery & Ordering from Natural Language Texts

TL;DR

GUIDO is a hybrid method combining machine learning and rule-based techniques to extract process models from natural language texts, achieving high accuracy with low annotation costs.

Contribution

The paper introduces GUIDO, a novel hybrid approach that improves process model extraction from texts by combining BERT-based classification with dependency parsing.

Findings

Achieves an average behavioral similarity score of 0.93

Outperforms purely rule-based approaches

Maintains low annotation costs compared to fully machine-learning methods

Abstract

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 results 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.