@inproceedings{hettiarachchi-ranasinghe-2019-emoji,
title = "Emoji Powered Capsule Network to Detect Type and Target of Offensive Posts in Social Media",
author = "Hettiarachchi, Hansi and
Ranasinghe, Tharindu",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
month = sep,
year = "2019",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/R19-1056",
doi = "10.26615/978-954-452-056-4_056",
pages = "474--480",
abstract = "This paper describes a novel research approach to detect type and target of offensive posts in social media using a capsule network. The input to the network was character embeddings combined with emoji embeddings. The approach was evaluated on all three subtasks in Task 6 - SemEval 2019: OffensEval: Identifying and Categorizing Offensive Language in Social Media. The evaluation also showed that even though the capsule networks have not been used commonly in natural language processing tasks, they can outperform existing state of the art solutions for offensive language detection in social media.",
}
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%0 Conference Proceedings
%T Emoji Powered Capsule Network to Detect Type and Target of Offensive Posts in Social Media
%A Hettiarachchi, Hansi
%A Ranasinghe, Tharindu
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F hettiarachchi-ranasinghe-2019-emoji
%X This paper describes a novel research approach to detect type and target of offensive posts in social media using a capsule network. The input to the network was character embeddings combined with emoji embeddings. The approach was evaluated on all three subtasks in Task 6 - SemEval 2019: OffensEval: Identifying and Categorizing Offensive Language in Social Media. The evaluation also showed that even though the capsule networks have not been used commonly in natural language processing tasks, they can outperform existing state of the art solutions for offensive language detection in social media.
%R 10.26615/978-954-452-056-4_056
%U https://aclanthology.org/R19-1056
%U https://doi.org/10.26615/978-954-452-056-4_056
%P 474-480
Markdown (Informal)
[Emoji Powered Capsule Network to Detect Type and Target of Offensive Posts in Social Media](https://aclanthology.org/R19-1056) (Hettiarachchi & Ranasinghe, RANLP 2019)
ACL