Building a Frame-Semantic Model of the Healthcare Domain: Towards the identification of gender-based violence in public health data

• Lívia Dutra UFJF / Göteborgs Universitet
• Arthur Lorenzi UFJF
• Lorena Larré UFJF https://orcid.org/0009-0006-1953-1321
• Frederico Belcavello UFJF
• Ely Matos UFJF
• Amanda Pestana UFJF
• Kenneth Brown UFJF
• Mariana Gonçalves UFJF
• Victor Herbst UFJF
• Sofia Reinach Vital Strategies Brasil
• Renato Teixeira Vital Strategies Brasil
• Pedro de Paula Vital Strategies Brasil
• Alessandra Pellini USCS
• Cibele Sequeira Secretaria da Saúde do Município de São Caetano do Sul
• Ester Sabino USCS
• Fábio Leal USCS
• Mônica Conde USCS
• Regina Grespan Secretaria da Saúde do Município de São Caetano do Sul
• Tiago Torrent UFJF / CNPq http://orcid.org/0000-0001-5373-2297

DOI: https://doi.org/10.5753/stil.2023.234145

Resumo

Public data systems gather a series of different information about Brazilian citizens. Such information is inserted in the system both via the selection of parameterized options and via open text fields. In this paper we describe the effort of modeling semantic frames for the lexicon of the healthcare domain as a means of tagging the open text fields in public health data so as to make them more easily interpretable by machine learning systems. This effort is one of the steps in a larger project aiming at using data science and machine learning techniques for the identification of territories prone to suffer from gender based-violence. The modeling effort currently covers 1,787 lexical units in the healthcare domain in Brazilian Portuguese, distributed in 29 semantic frames.

Palavras-chave: Semantics and Pragmatics, Corpus Annotation, Lexicography, Lexicology, Terminology

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