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.- Anthology ID:
- 2021.icon-multigen.8
- Volume:
- Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification
- Month:
- December
- Year:
- 2021
- Address:
- NIT Silchar
- Editors:
- Ritesh Kumar, Siddharth Singh, Enakshi Nandi, Shyam Ratan, Laishram Niranjana Devi, Bornini Lahiri, Akanksha Bansal, Akash Bhagat, Yogesh Dawer
- Venue:
- ICON
- SIG:
- Publisher:
- NLP Association of India (NLPAI)
- Note:
- Pages:
- 53–57
- Language:
- URL:
- https://aclanthology.org/2021.icon-multigen.8
- DOI:
- Cite (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).
- Cite (Informal):
- Sdutta at ComMA@ICON: A CNN-LSTM Model for Hate Detection (Dutta et al., ICON 2021)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2021.icon-multigen.8.pdf