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.

通过使用 BERT-based 句子分类器和依存句法分析，本文提出了一种名为 GUIDO 的混合方法来提取处理模型，取得了较纯粹基于规则的方法显著更好的结果，平均行为相似度得分为 0.93，并且相比于纯机器学习方法，标注成本低。

GUIDO: 从自然语言文本中发现和排序指南的混合方法

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

Process mining is a field of computer science that deals with discovery and
analysis of process models based on automatically generated event logs.
Currently, many companies use this technology for optimization and improving
their processes. However, a discovered process model may be too detailed,
sophisticated and difficult for experts to understand. In this paper, we
consider the problem of discovering a hierarchical business process model from
a low-level event log, i.e., the problem of automatic synthesis of more
readable and understandable process models based on information stored in event
logs of information systems.
Discovery of better structured and more readable process models is
intensively studied in the frame of process mining research from different
perspectives. In this paper, we present an algorithm for discovering
hierarchical process models represented as two-level workflow nets. The
algorithm is based on predefined event ilustering so that the cluster defines a
sub-process corresponding to a high-level transition at the top level of the
net. Unlike existing solutions, our algorithm does not impose restrictions on
the process control flow and allows for concurrency and iteration.

本文介绍了一种基于事件聚类的自动化合成算法，用于从底层事件日志中发现层次化业务流程模型，以便生成更易读和理解的过程模型。