@inproceedings{dutta-etal-2021-sdutta,
title = "Sdutta at {C}om{MA}@{ICON}: A {CNN}-{LSTM} Model for Hate Detection",
author = "Dutta, Sandip and
Majumder, Utso and
Naskar, Sudip",
editor = "Kumar, Ritesh and
Singh, Siddharth and
Nandi, Enakshi and
Ratan, Shyam and
Devi, Laishram Niranjana and
Lahiri, Bornini and
Bansal, Akanksha and
Bhagat, Akash and
Dawer, Yogesh",
booktitle = "Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification",
month = dec,
year = "2021",
address = "NIT Silchar",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2021.icon-multigen.8",
pages = "53--57",
abstract = "In today{'}s world, online activity and social media are facing an upsurge of cases of aggression, gender-biased comments and communal hate. In this shared task, we used a CNN-LSTM hybrid method to detect aggression, misogynistic and communally charged content in social media texts. First, we employ text cleaning and convert the text into word embeddings. Next we proceed to our CNN-LSTM based model to predict the nature of the text. Our model achieves 0.288, 0.279, 0.294 and 0.335 Overall Micro F1 Scores in multilingual, Meitei, Bengali and Hindi datasets, respectively, on the 3 prediction labels.",
}
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%0 Conference Proceedings
%T Sdutta at ComMA@ICON: A CNN-LSTM Model for Hate Detection
%A Dutta, Sandip
%A Majumder, Utso
%A Naskar, Sudip
%Y Kumar, Ritesh
%Y Singh, Siddharth
%Y Nandi, Enakshi
%Y Ratan, Shyam
%Y Devi, Laishram Niranjana
%Y Lahiri, Bornini
%Y Bansal, Akanksha
%Y Bhagat, Akash
%Y Dawer, Yogesh
%S Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C NIT Silchar
%F dutta-etal-2021-sdutta
%X In today’s world, online activity and social media are facing an upsurge of cases of aggression, gender-biased comments and communal hate. In this shared task, we used a CNN-LSTM hybrid method to detect aggression, misogynistic and communally charged content in social media texts. First, we employ text cleaning and convert the text into word embeddings. Next we proceed to our CNN-LSTM based model to predict the nature of the text. Our model achieves 0.288, 0.279, 0.294 and 0.335 Overall Micro F1 Scores in multilingual, Meitei, Bengali and Hindi datasets, respectively, on the 3 prediction labels.
%U https://aclanthology.org/2021.icon-multigen.8
%P 53-57
Markdown (Informal)
[Sdutta at ComMA@ICON: A CNN-LSTM Model for Hate Detection](https://aclanthology.org/2021.icon-multigen.8) (Dutta et al., ICON 2021)
ACL
- Sandip Dutta, Utso Majumder, and Sudip Naskar. 2021. Sdutta at ComMA@ICON: A CNN-LSTM Model for Hate Detection. In Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification, pages 53–57, NIT Silchar. NLP Association of India (NLPAI).