TY - GEN
T1 - Emoji Powered Capsule Network to Detect Type and Target of Offensive Posts in Social Media
AU - Ranasinghe, Tharindu
AU - Hettiarachchi, Hansi
N1 - ACL materials are Copyright © 1963–2023 ACL. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
UR - https://www.aclweb.org/anthology/R19-1056
U2 - 10.26615/978-954-452-056-4_056
DO - 10.26615/978-954-452-056-4_056
M3 - Conference publication
SP - 474
EP - 480
BT - International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
ER -