Abstract
This paper describes our participation in the SemEval 2019 Task 5 - Multilingual Detection of Hate. This task aims to identify hate speech against two specific targets, immigrants and women. We compare and contrast the performance of different word and sentence level embeddings on the state-of-the-art classification algorithms. Our final submission is a Multinomial binarized Naive Bayes model for both the subtasks in the English version.- Anthology ID:
- S19-2071
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Editors:
- Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 404–408
- Language:
- URL:
- https://aclanthology.org/S19-2071
- DOI:
- 10.18653/v1/S19-2071
- Cite (ACL):
- Nikhil Chakravartula. 2019. HATEMINER at SemEval-2019 Task 5: Hate speech detection against Immigrants and Women in Twitter using a Multinomial Naive Bayes Classifier. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 404–408, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- HATEMINER at SemEval-2019 Task 5: Hate speech detection against Immigrants and Women in Twitter using a Multinomial Naive Bayes Classifier (Chakravartula, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/S19-2071.pdf