@inproceedings{dutra-etal-2026-evaluating,
title = "Evaluating {F}rame{N}et-Based Semantic Modeling for Gender-Based Violence Detection in Clinical Records",
author = "Dutra, L{\'i}via and
Lorenzi, Arthur and
Belcavello, Frederico and
Matos, Ely and
Viridiano, Marcelo and
Larr{\'e}, Lorena and
Guaranha, Ol{\'i}via and
Santos, Erick and
Reinach, Sofia and
Paula, Pedro de and
Torrent, Tiago",
editor = "Souza, Marlo and
de-Dios-Flores, Iria and
Santos, Diana and
Freitas, Larissa and
Souza, Jackson Wilke da Cruz and
Ribeiro, Eug{\'e}nio",
booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 2",
month = apr,
year = "2026",
address = "Salvador, Brazil",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.propor-2.12/",
pages = "49--57",
ISBN = "979-8-89176-387-6",
abstract = "Gender-based violence (GBV) is a major public health issue, with the World Health Organization estimating that one in three women experiences physical or sexual violence by an intimate partner during her lifetime. In Brazil, although healthcare professionals are legally required to report such cases, underreporting remains significant due to difficulties in identifying abuse and limited integration between public information systems. This study investigates whether FrameNet-based semantic annotation of open-text fields in electronic medical records can support the identification of patterns of GBV. We compare the performance of an SVM classifier for GBV cases trained on (1) frame-annotated text, (2) annotated text combined with parameterized data, and (3) parameterized data alone. Quantitative and qualitative analyses show that models incorporating semantic annotation outperform categorical models, achieving over 0.3 improvement in F1 score and demonstrating that domain-specific semantic representations provide meaningful signals beyond structured demographic data. The findings support the hypothesis that semantic analysis of clinical narratives can enhance early identification strategies and support more informed public health interventions."
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%0 Conference Proceedings
%T Evaluating FrameNet-Based Semantic Modeling for Gender-Based Violence Detection in Clinical Records
%A Dutra, Lívia
%A Lorenzi, Arthur
%A Belcavello, Frederico
%A Matos, Ely
%A Viridiano, Marcelo
%A Larré, Lorena
%A Guaranha, Olívia
%A Santos, Erick
%A Reinach, Sofia
%A Paula, Pedro de
%A Torrent, Tiago
%Y Souza, Marlo
%Y de-Dios-Flores, Iria
%Y Santos, Diana
%Y Freitas, Larissa
%Y Souza, Jackson Wilke da Cruz
%Y Ribeiro, Eugénio
%S Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 2
%D 2026
%8 April
%I Association for Computational Linguistics
%C Salvador, Brazil
%@ 979-8-89176-387-6
%F dutra-etal-2026-evaluating
%X Gender-based violence (GBV) is a major public health issue, with the World Health Organization estimating that one in three women experiences physical or sexual violence by an intimate partner during her lifetime. In Brazil, although healthcare professionals are legally required to report such cases, underreporting remains significant due to difficulties in identifying abuse and limited integration between public information systems. This study investigates whether FrameNet-based semantic annotation of open-text fields in electronic medical records can support the identification of patterns of GBV. We compare the performance of an SVM classifier for GBV cases trained on (1) frame-annotated text, (2) annotated text combined with parameterized data, and (3) parameterized data alone. Quantitative and qualitative analyses show that models incorporating semantic annotation outperform categorical models, achieving over 0.3 improvement in F1 score and demonstrating that domain-specific semantic representations provide meaningful signals beyond structured demographic data. The findings support the hypothesis that semantic analysis of clinical narratives can enhance early identification strategies and support more informed public health interventions.
%U https://aclanthology.org/2026.propor-2.12/
%P 49-57
Markdown (Informal)
[Evaluating FrameNet-Based Semantic Modeling for Gender-Based Violence Detection in Clinical Records](https://aclanthology.org/2026.propor-2.12/) (Dutra et al., PROPOR 2026)
ACL
- Lívia Dutra, Arthur Lorenzi, Frederico Belcavello, Ely Matos, Marcelo Viridiano, Lorena Larré, Olívia Guaranha, Erick Santos, Sofia Reinach, Pedro de Paula, and Tiago Torrent. 2026. Evaluating FrameNet-Based Semantic Modeling for Gender-Based Violence Detection in Clinical Records. In Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 2, pages 49–57, Salvador, Brazil. Association for Computational Linguistics.