Negation is a central phenomenon in linguistics: every language has some way of expressing the difference between an affirmative sentence and a negative one (Horn and Wansing, 2025). However, the treatment of negation remains uneven in Natural Language Processing (Jimenez-Zafra et al., 2017; Jiménez-Zafra et al., 2020). This paper presents the enrichment of a Brazilian Portuguese corpus with negation-related morphological information within the Universal Dependencies (UD) framework (Nivre et al., 2020; de Marneffe et al., 2021). We enrich the Porttinari-base corpus (Duran et al., 2023) by systematically adding the UD morphological features Polarity=Neg and PronType=Neg for 18 negation-related lexical items. The enrichment only modifies the morphological features, leaving tokenization and dependency structure unchanged. To evaluate the computational results of this enrichment, we present an experiment using the Brazilian Portuguese parser PortParser (Lopes and Pardo, 2024), which we trained both on the original Porttinari-base data (Duran et al., 2023) and on our enriched version. Our results show that after enrichment, the parser’s performance remains stable, and the newly introduced features are being learned.