Scmhl5 at TRAC-2 shared task on aggression identification: bert based ensemble learning approach

Liu, Han ORCID: https://orcid.org/0000-0002-7731-8258, Burnap, Peter ORCID: https://orcid.org/0000-0003-0396-633X, Alorainy, Wafa and Williams, Matthew ORCID: https://orcid.org/0000-0003-2566-6063 2020. Scmhl5 at TRAC-2 shared task on aggression identification: bert based ensemble learning approach. Presented at: Second Workshop on Trolling, Aggression and Cyberbullying, Marseille, France, 16 May 2020.

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Abstract

This paper presents a system developed during our participation (team name: scmhl5) in the TRAC-2 Shared Task on aggression identification. In particular, we participated in English Sub-task A on three-class classification ('Overtly Aggressive', 'Covertly Aggressive' and 'Non-aggressive') and English Sub-task B on binary classification for Misogynistic Aggression ('gendered' or 'non-gendered'). For both sub-tasks, our method involves using the pre-trained Bert model for extracting the text of each instance into a 768-dimensional vector of embeddings, and then training an ensemble of classifiers on the embedding features. Our method obtained accuracy of 0.703 and weighted F-measure of 0.664 for Sub-task A, whereas for Sub-task B the accuracy was 0.869 and weighted F-measure was 0.851. In terms of the rankings, the weighted F-measure obtained using our method for Sub-task A is ranked in the 10th out of 16 teams, whereas for Sub-task B the weighted F-measure is ranked in the 8th out of 15 teams.

Item Type:	Conference or Workshop Item (Paper)
Status:	In Press
Schools:	Schools > Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Related URLs:	Organisation
Date of First Compliant Deposit:	17 April 2020
Date of Acceptance:	11 April 2020
Last Modified:	05 Jan 2024 06:17
URI:	https://orca.cardiff.ac.uk/id/eprint/131040

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