Could you tell me the process ID? Structuring Text Documents from the Brazilian Electronic Information System Using a Named Entity Recognition Approach

Context: In the context of valuing open data, transparency, and efficiency in public services, there is a growing demand for studies to improve government systems. Some public agencies use the Brazilian Electronic Information System (Sistema Eletrônico de Informações - SEI), a procedural management system that centralizes electronic processes and promotes administrative efficiency. Problem: Although SEI has contributed to advancements in public administration, significant challenges remain in information search and retrieval due to the inefficient keyword-based approach currently available. These difficulties are enhanced by the high amount of documents generated daily, which are written in natural language and present variability in categories, writing styles, and structures. As a result, searching for relevant documents in SEI is time-consuming, leading users to create unnecessary processes and inconsistent resolutions when compared to previously completed processes. Solution: A Natural Language Processing (NLP) pipeline was proposed to extract information from SEI documents using Named Entity Recognition (NER) models. IS Theory: Organizational Information Processing. Method: This research adopts a descriptive approach. Public SEI documents were collected using a web crawler, and trained annotators built a corpus to enable the training of state-of-the-art NER models. The models’ performances were compared and quantitatively analyzed. Summary of Results: A Brazilian Portuguese labeled corpus of SEI for NER was curated and validated, leading to an NLP pipeline for information extraction. Contributions and Impact in the IS area: This research provides a baseline for structuring data from Electronic Information Systems, enabling more effective strategies for search and retrieval tasks.