nlpUP at SemEval-2020 Task 12 : A Blazing Fast System for Offensive Language Detection

Ehab Hamdy, Jelena Mitrović, Michael Granitzer


Abstract
In this paper, we introduce our submission for the SemEval Task 12, sub-tasks A and B for offensive language identification and categorization in English tweets. This year the data set for Task A is significantly larger than in the previous year. Therefore, we have adapted the BlazingText algorithm to extract embedding representation and classify texts after filtering and sanitizing the dataset according to the conventional text patterns on social media. We have gained both advantages of a speedy training process and obtained a good F1 score of 90.88% on the test set. For sub-task B, we opted to fine-tune a Bidirectional Encoder Representation from a Transformer (BERT) to accommodate the limited data for categorizing offensive tweets. We have achieved an F1 score of only 56.86%, but after experimenting with various label assignment thresholds in the pre-processing steps, the F1 score improved to 64%.
Anthology ID:
2020.semeval-1.278
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
2098–2104
Language:
URL:
https://aclanthology.org/2020.semeval-1.278
DOI:
10.18653/v1/2020.semeval-1.278
Bibkey:
Cite (ACL):
Ehab Hamdy, Jelena Mitrović, and Michael Granitzer. 2020. nlpUP at SemEval-2020 Task 12 : A Blazing Fast System for Offensive Language Detection. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 2098–2104, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
nlpUP at SemEval-2020 Task 12 : A Blazing Fast System for Offensive Language Detection (Hamdy et al., SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.278.pdf
Data
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