Authors:
Diogo de Santana Candido
1
;
2
;
Hilário Tomaz Alves de Oliveira
1
and
Mateus Barcellos Costa
1
Affiliations:
1
Postgraduate Program in Applied Computing (PPComp), Instituto Federal do Espírito Santo, Serra, Brazil
;
2
Senado Federal, Brasília, Brazil
Keyword(s):
Business Process Modeling, Natural Language Processing, Named Entity Recognition, Relation Classification, Machine Learning.
Abstract:
Business process models have increasingly been recognized as critical artifacts for organizations. However, process modeling, i.e., the act of creating accurate and meaningful models, remains a significant challenge. As a result, many processes continue to be informally described using natural language text, leading to ambiguities and hindering precise modeling. To address these issues, more formalized models are typically developed manually, a task that requires substantial time and effort. This study proposes a transcription approach that leverages Natural Language Processing (NLP) techniques for the preliminary extraction of entities and constraint relations. A dataset comprising 133 documents annotated with 5,395 expert labels was utilized to evaluate the effectiveness of the proposed method. The experiments focused on two primary tasks: Named Entity Recognition (NER) and relation classification. For NER, the BiLSTM-CRF model, enhanced with Glove and Flair embeddings, delivered t
he best performance. In the relation classification task, the RoBERTaLarge model achieved superior results, particularly in managing complex dependencies. These findings highlight the potential of NLP techniques to automate and enhance business process modeling.
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