MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter

Debanjan Mahata, Haimin Zhang, Karan Uppal, Yaman Kumar, Rajiv Ratn Shah, Simra Shahid, Laiba Mehnaz, Sarthak Anand


Abstract
In this paper we present our approach and the system description for Sub Task A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media. Sub Task A involves identifying if a given tweet is offensive and Sub Task B involves detecting if an offensive tweet is targeted towards someone (group or an individual). Our models for Sub Task A is based on an ensemble of Convolutional Neural Network and Bidirectional LSTM, whereas for Sub Task B, we rely on a set of heuristics derived from the training data. We provide detailed analysis of the results obtained using the trained models. Our team ranked 5th out of 103 participants in Sub Task A, achieving a macro F1 score of 0.807, and ranked 8th out of 75 participants achieving a macro F1 of 0.695.
Anthology ID:
S19-2122
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
683–690
Language:
URL:
https://aclanthology.org/S19-2122
DOI:
10.18653/v1/S19-2122
Bibkey:
Cite (ACL):
Debanjan Mahata, Haimin Zhang, Karan Uppal, Yaman Kumar, Rajiv Ratn Shah, Simra Shahid, Laiba Mehnaz, and Sarthak Anand. 2019. MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 683–690, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter (Mahata et al., SemEval 2019)
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PDF:
https://aclanthology.org/S19-2122.pdf