Emoji Powered Capsule Network to Detect Type and Target of Offensive Posts in Social Media


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.

Publication DOI: https://doi.org/10.26615/978-954-452-056-4_056
Additional Information: ACL materials are Copyright © 1963–2023 ACL. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.
Last Modified: 24 Apr 2024 17:51
Date Deposited: 24 Jan 2023 17:10
Full Text Link: https://www.acl ... hology/R19-1056
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PURE Output Type: Conference contribution
Published Date: 2019-09
Authors: Ranasinghe, Tharindu (ORCID Profile 0000-0003-3207-3821)
Hettiarachchi, Hansi



Version: Published Version

License: Creative Commons Attribution

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