Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text
arxiv(2023)
摘要
Process-aware information systems offer extensive advantages to companies,
facilitating planning, operations, and optimization of day-to-day business
activities. However, the time-consuming but required step of designing formal
business process models often hampers the potential of these systems. To
overcome this challenge, automated generation of business process models from
natural language text has emerged as a promising approach to expedite this
step. Generally two crucial subtasks have to be solved: extracting
process-relevant information from natural language and creating the actual
model. Approaches towards the first subtask are rule based methods, highly
optimized for specific domains, but hard to adapt to related applications. To
solve this issue, we present an extension to an existing pipeline, to make it
entirely data driven. We demonstrate the competitiveness of our improved
pipeline, which not only eliminates the substantial overhead associated with
feature engineering and rule definition, but also enables adaptation to
different datasets, entity and relation types, and new domains. Additionally,
the largest available dataset (PET) for the first subtask, contains no
information about linguistic references between mentions of entities in the
process description. Yet, the resolution of these mentions into a single visual
element is essential for high quality process models. We propose an extension
to the PET dataset that incorporates information about linguistic references
and a corresponding method for resolving them. Finally, we provide a detailed
analysis of the inherent challenges in the dataset at hand.
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