bhanodaig at SemEval-2019 Task 6: Categorizing Offensive Language in social media

Ritesh Kumar, Guggilla Bhanodai, Rajendra Pamula, Maheswara Reddy Chennuru


Abstract
This paper describes the work that our team bhanodaig did at Indian Institute of Technology (ISM) towards OffensEval i.e. identifying and categorizing offensive language in social media. Out of three sub-tasks, we have participated in sub-task B: automatic categorization of offensive types. We perform the task of categorizing offensive language, whether the tweet is targeted insult or untargeted. We use Linear Support Vector Machine for classification. The official ranking metric is macro-averaged F1. Our system gets the score 0.5282 with accuracy 0.8792. However, as new entrant to the field, our scores are encouraging enough to work for better results in future.
Anthology ID:
S19-2098
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:
547–550
Language:
URL:
https://aclanthology.org/S19-2098
DOI:
10.18653/v1/S19-2098
Bibkey:
Cite (ACL):
Ritesh Kumar, Guggilla Bhanodai, Rajendra Pamula, and Maheswara Reddy Chennuru. 2019. bhanodaig at SemEval-2019 Task 6: Categorizing Offensive Language in social media. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 547–550, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
bhanodaig at SemEval-2019 Task 6: Categorizing Offensive Language in social media (Kumar et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2098.pdf