Automated generation of business process models from natural language text is
an emerging methodology for avoiding the manual creation of formal business
process models. For this purpose, process entities like actors, activities,
objects etc., and relations among them are extracted from textual process
descriptions. A high-quality annotated corpus of textual process descriptions
(PET) has been published accompanied with a basic process extraction approach.
In its current state, however, PET lacks information about whether two mentions
refer to the same or different process entities, which corresponds to the
crucial decision of whether to create one or two modeling elements in the
target model. Consequently, it is ambiguous whether, for instance, two mentions
of data processing mean processing of different, or the same data. In this
paper, we extend the PET dataset by clustering mentions of process entities and
by proposing a new baseline technique for process extraction equipped with an
additional entity resolution component. In a second step, we replace the
rule-based relation extraction component with a machine learning-based
alternative, enabling rapid adaption to different datasets and domains. In
addition, we evaluate a deep learning-approach built for solving entity and
relation extraction as well as entity resolution in a holistic manner. Finally,
our extensive evaluation of the original PET baseline against our own
implementation shows that a pure machine learning-based process extraction
technique is competitive, while avoiding the massive overhead arising from
feature engineering and rule definition needed to adapt to other datasets,
different entity and relation types, or new domains.

通过提出一种新的基线技术和构建基于机器学习的过程提取技术，将实体解析组件与实体和关系提取组件进行深度融合，以适应不同数据集和领域，从而避免了适应其他数据集、不同实体和关系类型或新领域所需的特征工程和规则定义所产生的巨大开销。