@inproceedings{saraiva-etal-2021-semi,
title = "A Semi-Supervised Approach to Detect Toxic Comments",
author = "Saraiva, Ghivvago Damas and
Anchi{\^e}ta, Rafael and
Neto, Francisco Assis Ricarte and
Moura, Raimundo",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
month = sep,
year = "2021",
address = "Held Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-1.142",
pages = "1261--1267",
abstract = "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.",
}
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%0 Conference Proceedings
%T A Semi-Supervised Approach to Detect Toxic Comments
%A Saraiva, Ghivvago Damas
%A Anchiêta, Rafael
%A Neto, Francisco Assis Ricarte
%A Moura, Raimundo
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F saraiva-etal-2021-semi
%X 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.
%U https://aclanthology.org/2021.ranlp-1.142
%P 1261-1267
Markdown (Informal)
[A Semi-Supervised Approach to Detect Toxic Comments](https://aclanthology.org/2021.ranlp-1.142) (Saraiva et al., RANLP 2021)
ACL
- Ghivvago Damas Saraiva, Rafael Anchiêta, Francisco Assis Ricarte Neto, and Raimundo Moura. 2021. A Semi-Supervised Approach to Detect Toxic Comments. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1261–1267, Held Online. INCOMA Ltd..