JCTICOL at SemEval-2019 Task 6: Classifying Offensive Language in Social Media using Deep Learning Methods, Word/Character N-gram Features, and Preprocessing Methods

Yaakov HaCohen-Kerner, Ziv Ben-David, Gal Didi, Eli Cahn, Shalom Rochman, Elyashiv Shayovitz


Abstract
In this paper, we describe our submissions to SemEval-2019 task 6 contest. We tackled all three sub-tasks in this task “OffensEval - Identifying and Categorizing Offensive Language in Social Media”. In our system called JCTICOL (Jerusalem College of Technology Identifies and Categorizes Offensive Language), we applied various supervised ML methods. We applied various combinations of word/character n-gram features using the TF-IDF scheme. In addition, we applied various combinations of seven basic preprocessing methods. Our best submission, an RNN model was ranked at the 25th position out of 65 submissions for the most complex sub-task (C).
Anthology ID:
S19-2115
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
645–651
Language:
URL:
https://aclanthology.org/S19-2115
DOI:
10.18653/v1/S19-2115
Bibkey:
Cite (ACL):
Yaakov HaCohen-Kerner, Ziv Ben-David, Gal Didi, Eli Cahn, Shalom Rochman, and Elyashiv Shayovitz. 2019. JCTICOL at SemEval-2019 Task 6: Classifying Offensive Language in Social Media using Deep Learning Methods, Word/Character N-gram Features, and Preprocessing Methods. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 645–651, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
JCTICOL at SemEval-2019 Task 6: Classifying Offensive Language in Social Media using Deep Learning Methods, Word/Character N-gram Features, and Preprocessing Methods (HaCohen-Kerner et al., SemEval 2019)
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PDF:
https://preview.aclanthology.org/update-css-js/S19-2115.pdf