Computational Approaches for Simplifying Educational Texts: A Proposal Using spaCy - Souza, Vitor Amadeu (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2025: Proceedings of the 31st Brazilian Symposium on Multimedia and the WebDO - 10.5753/webmedia.2025.15154, 2025)
This work presents an investigation into the application of Natural Language Processing (NLP) techniques for the automatic simplification of educational texts in Brazilian Portuguese. The study uses the spaCy library with the pt_core_news_sm model to perform syntactic analysis, named entity recognition, and textual readability assessment. The proposed methodology implements simplification rules based on syntactic dependency analysis, preserving essential elements such as the subject and main predicate while removing complex subordinate constructions. The results show that named entity analysis was effective in identifying people (PER), locations (LOC), organizations (ORG), and miscellaneous elements (MISC) in the analyzed texts. The original texts presented Flesch Reading Ease scores ranging from 25.23 to 54.57, indicating different levels of complexity. This research contributes to the advancement of automatic text simplification techniques in Portuguese and offers insights for the development of more accessible educational tools.