Abstract
This article presents our approach for detecting a target of offensive messages in Twitter, including Individual, Group and Others classes. The model we have created is an ensemble of simpler models, including Logistic Regression, Naive Bayes, Support Vector Machine and the interpolation between Logistic Regression and Naive Bayes with 0.25 coefficient of interpolation. The model allows us to achieve 0.547 macro F1-score.
Cite
CITATION STYLE
Shushkevich, E., Cardiff, J., & Rosso, P. (2019). TUVD team at SemEval-2019 task 6: Offense target identification. In NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop (pp. 770–774). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s19-2135
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