Gal Didi


2019

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JCTDHS at SemEval-2019 Task 5: Detection of Hate Speech in Tweets using Deep Learning Methods, Character N-gram Features, and Preprocessing Methods
Yaakov HaCohen-Kerner | Elyashiv Shayovitz | Shalom Rochman | Eli Cahn | Gal Didi | Ziv Ben-David
Proceedings of the 13th International Workshop on Semantic Evaluation

In this paper, we describe our submissions to SemEval-2019 contest. We tackled subtask A - “a binary classification where systems have to predict whether a tweet with a given target (women or immigrants) is hateful or not hateful”, a part of task 5 “Multilingual detection of hate speech against immigrants and women in Twitter (hatEval)”. Our system JCTDHS (Jerusalem College of Technology Detects Hate Speech) was developed for tweets written in English. We applied various supervised ML methods, various combinations of n-gram features using the TF-IDF scheme and. In addition, we applied various combinations of eight basic preprocessing methods. Our best submission was a special bidirectional RNN, which was ranked at the 11th position out of 68 submissions.

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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
Proceedings of the 13th International Workshop on Semantic Evaluation

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).