DEBISS-Arg: An In Depth Data Annotation Protocol and Corpus for Argument Mining in Semi Structured Debates - Pereira, David Eduardo; Simão, Daniela Thuaslar; Campelo, Claudio E. C. (Anais do Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL), 2025)
Argumentation plays a crucial role across various domains of human activity. However, its diverse applications present challenges in knowledge representation, leading to the development of numerous data models without a universally accepted standard. This lack of standardization complicates the creation of data and representation frameworks for argument annotation, which are essential for building high-quality datasets. This research addresses these limitations by proposing a comprehensive data annotation protocol specifically designed for argument mining in semi-structured debates. The protocol is applied to the newly introduced DEBISS-Arg corpus, which includes multiple annotation labels covering a range of argument mining tasks in Brazilian Portuguese.