An Annotated Dataset for Automatic Extraction of Entities and Restrictions from Business Process Models

• Diogo S. Candido IFES
• João Victor Berti Lima IFES
• Hilário Oliveira IFES
• Mateus B. Costa IFES

DOI: https://doi.org/10.5753/eniac.2024.245085

Abstract

Business Process Modeling is often perceived as a high-potential activity that is difficult to implement. Various techniques and methods have been proposed and investigated to support this activity, with emphasis on the use of natural language processing techniques. However, the scarcity of datasets specifically for this purpose constitutes an important limitation recognized by the literature. This work proposes an annotated dataset for identifying typical business process entities and restrictions. Experiments conducted focusing on entity recognition suggest that the BiLSTM-CRF architecture, with word embeddings extracted from the GloVe, Flair, and BERT models, achieved the best performance based on the micro average of the f1-score measure.

Keywords: Business Process Modeling, Natural Language Processing, Named Entity Recognition

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