Abstract
In this paper, we present the system description of Offensive language detection tool which is developed by the KMI_Coling under the OffensEval Shared task. The OffensEval Shared Task was conducted in SemEval 2019 workshop. To develop the system, we have explored n-grams up to 8-gram and trained three different namely A, B and C systems for three different subtasks within the OffensEval task which achieves 79.76%, 87.91% and 44.37% accuracy respectively. The task was completed using the dataset provided to us by the OffensEval organisers was the part of OLID dataset. It consists of 13,240 tweets extracted from twitter and were annotated at three levels using crowdsourcing.- Anthology ID:
- S19-2119
- 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:
- 668–671
- Language:
- URL:
- https://aclanthology.org/S19-2119
- DOI:
- 10.18653/v1/S19-2119
- Cite (ACL):
- Priya Rani and Atul Kr. Ojha. 2019. KMI-Coling at SemEval-2019 Task 6: Exploring N-grams for Offensive Language detection. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 668–671, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- KMI-Coling at SemEval-2019 Task 6: Exploring N-grams for Offensive Language detection (Rani & Ojha, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/S19-2119.pdf
- Data
- OLID