@inproceedings{mahata-etal-2019-midas,
title = "{MIDAS} at {S}em{E}val-2019 Task 6: Identifying Offensive Posts and Targeted Offense from {T}witter",
author = "Mahata, Debanjan and
Zhang, Haimin and
Uppal, Karan and
Kumar, Yaman and
Shah, Rajiv Ratn and
Shahid, Simra and
Mehnaz, Laiba and
Anand, Sarthak",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2122",
doi = "10.18653/v1/S19-2122",
pages = "683--690",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter
%A Mahata, Debanjan
%A Zhang, Haimin
%A Uppal, Karan
%A Kumar, Yaman
%A Shah, Rajiv Ratn
%A Shahid, Simra
%A Mehnaz, Laiba
%A Anand, Sarthak
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F mahata-etal-2019-midas
%X 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.
%R 10.18653/v1/S19-2122
%U https://aclanthology.org/S19-2122
%U https://doi.org/10.18653/v1/S19-2122
%P 683-690
Markdown (Informal)
[MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter](https://aclanthology.org/S19-2122) (Mahata et al., SemEval 2019)
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.