TUVD team at SemEval-2019 Task 6: Offense Target Identification

Elena Shushkevich, John Cardiff, Paolo Rosso

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.
Anthology ID:
S19-2135
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:
770–774
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/S19-2135/
DOI:
10.18653/v1/S19-2135
Bibkey:
Cite (ACL):
Elena Shushkevich, John Cardiff, and Paolo Rosso. 2019. TUVD team at SemEval-2019 Task 6: Offense Target Identification. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 770–774, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
TUVD team at SemEval-2019 Task 6: Offense Target Identification (Shushkevich et al., SemEval 2019)
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PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/S19-2135.pdf
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