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    <abstract>In this paper we built several deep learning architectures to participate in shared task OffensEval: Identifying and categorizing Offensive language in Social media by semEval-2019. The dataset was annotated with three level annotation schemes and task was to detect between offensive and not offensive, categorization and target identification in offensive contents. Deep learning models with POS information as feature were also leveraged for classification. The three best models that performed best on individual sub tasks are stacking of CNN-Bi-LSTM with Attention, BiLSTM with POS information added with word features and Bi-LSTM for third task. Our models achieved a Macro F1 score of 0.7594, 0.5378 and 0.4588 in Task(A,B,C) respectively with rank of 33rd, 54th and 52nd out of 103, 75 and 65 submissions. The three best models that performed best on individual sub task are using Neural Networks.</abstract>
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%0 Conference Proceedings
%T NLP at SemEval-2019 Task 6: Detecting Offensive language using Neural Networks
%A Kapil, Prashant
%A Ekbal, Asif
%A Das, Dipankar
%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 kapil-etal-2019-nlp
%X In this paper we built several deep learning architectures to participate in shared task OffensEval: Identifying and categorizing Offensive language in Social media by semEval-2019. The dataset was annotated with three level annotation schemes and task was to detect between offensive and not offensive, categorization and target identification in offensive contents. Deep learning models with POS information as feature were also leveraged for classification. The three best models that performed best on individual sub tasks are stacking of CNN-Bi-LSTM with Attention, BiLSTM with POS information added with word features and Bi-LSTM for third task. Our models achieved a Macro F1 score of 0.7594, 0.5378 and 0.4588 in Task(A,B,C) respectively with rank of 33rd, 54th and 52nd out of 103, 75 and 65 submissions. The three best models that performed best on individual sub task are using Neural Networks.
%R 10.18653/v1/S19-2105
%U https://aclanthology.org/S19-2105
%U https://doi.org/10.18653/v1/S19-2105
%P 587-592

Markdown (Informal)

[NLP at SemEval-2019 Task 6: Detecting Offensive language using Neural Networks](https://aclanthology.org/S19-2105) (Kapil et al., SemEval 2019)

• NLP at SemEval-2019 Task 6: Detecting Offensive language using Neural Networks (Kapil et al., SemEval 2019)

ACL

• Prashant Kapil, Asif Ekbal, and Dipankar Das. 2019. NLP at SemEval-2019 Task 6: Detecting Offensive language using Neural Networks. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 587–592, Minneapolis, Minnesota, USA. Association for Computational Linguistics.