Fermi at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Sentence Embeddings
Vijayasaradhi Indurthi, Bakhtiyar Syed, Manish Shrivastava, Manish Gupta, Vasudeva Varma
Abstract
This paper describes our system (Fermi) for Task 6: OffensEval: Identifying and Categorizing Offensive Language in Social Media of SemEval-2019. We participated in all the three sub-tasks within Task 6. We evaluate multiple sentence embeddings in conjunction with various supervised machine learning algorithms and evaluate the performance of simple yet effective embedding-ML combination algorithms. Our team Fermi’s model achieved an F1-score of 64.40%, 62.00% and 62.60% for sub-task A, B and C respectively on the official leaderboard. Our model for sub-task C which uses pre-trained ELMo embeddings for transforming the input and uses SVM (RBF kernel) for training, scored third position on the official leaderboard. Through the paper we provide a detailed description of the approach, as well as the results obtained for the task.- Anthology ID:
- S19-2109
- 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:
- 611–616
- Language:
- URL:
- https://aclanthology.org/S19-2109
- DOI:
- 10.18653/v1/S19-2109
- Cite (ACL):
- Vijayasaradhi Indurthi, Bakhtiyar Syed, Manish Shrivastava, Manish Gupta, and Vasudeva Varma. 2019. Fermi at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Sentence Embeddings. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 611–616, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- Fermi at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Sentence Embeddings (Indurthi et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/S19-2109.pdf