Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
12-2020
Abstract
This paper describes the systems our team (AdelaideCyC) has developed for SemEval Task 12 (OffensEval 2020) to detect offensive language in social media. The challenge focuses on three subtasks – offensive language identification (subtask A), offense type identification (subtask B), and offense target identification (subtask C). Our team has participated in all the three subtasks. We have developed machine learning and deep learning-based ensembles of models. We have achieved F1-scores of 0.906, 0.552, and 0.623 in subtask A, B, and C respectively. While our performance scores are promising for subtask A, the results demonstrate that subtask B and C still remain challenging to classify.
Discipline
Social Media | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of the 14th International Workshop on Semantic Evaluation, Barcelona, 2020 December 12
First Page
1516
Last Page
1523
ISBN
9781952148316
Identifier
10.18653/v1/2020.semeval-1.198
Publisher
International Committee for Computational Linguistics
City or Country
Barcelona, Spain
Citation
HERATH, Mahen; ATAPATTU, Thushari; DUNG, Hoang Anh; TREUDE, Christoph; and FALKNER, Katrina.
AdelaideCyC at SemEval-2020 Task 12: Ensemble of classifiers for offensive language detection in social media. (2020). Proceedings of the 14th International Workshop on Semantic Evaluation, Barcelona, 2020 December 12. 1516-1523.
Available at: https://ink.library.smu.edu.sg/sis_research/8931
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.18653/v1/2020.semeval-1.198