Raimundo Moura


A Semi-Supervised Approach to Detect Toxic Comments
Ghivvago Damas Saraiva | Rafael Anchiêta | Francisco Assis Ricarte Neto | Raimundo Moura
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Toxic comments contain forms of non-acceptable language targeted towards groups or individuals. These types of comments become a serious concern for government organizations, online communities, and social media platforms. Although there are some approaches to handle non-acceptable language, most of them focus on supervised learning and the English language. In this paper, we deal with toxic comment detection as a semi-supervised strategy over a heterogeneous graph. We evaluate the approach on a toxic dataset of the Portuguese language, outperforming several graph-based methods and achieving competitive results compared to transformer architectures.


Improving Opinion Summarization by Assessing Sentence Importance in On-line Reviews
Rafael Anchiêta | Rogerio Figueredo Sousa | Raimundo Moura | Thiago Pardo
Proceedings of the 11th Brazilian Symposium in Information and Human Language Technology