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.
Cite
CITATION STYLE
Kumar, R., Bhanodai, G., Pamula, R., & Chennuru, M. R. (2019). bhanodaig at SemEval-2019 task 6: Categorizing offensive language in social media. In NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop (pp. 547–550). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s19-2098
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