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

Additional URL

https://doi.org/10.18653/v1/2020.semeval-1.198

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