首页> 外文会议>Annual conference of the North American Chapter of the Association for Computational Linguistics: human language technologies;International workshop on semantic evaluation >JCTICOL at SemEval-2019 Task 6: Classifying Offensive Language in Social Media using Deep Learning Methods, Word/Character N-gram Features, and Preprocessing Methods
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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

机译:JCTICOL在SemEval-2019上的任务6:使用深度学习方法,单词/字符N-gram功能和预处理方法在社交媒体中对攻击性语言进行分类

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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).
机译:在本文中,我们描述了我们对SemEval-2019任务6竞赛的提交。我们在“ OffensEval-在社交媒体中识别和分类攻击性语言”任务中解决了所有三个子任务。在我们称为JCTICOL(耶路撒冷科技大学识别并分类进攻性语言)的系统中,我们应用了各种监督的ML方法。我们使用TF-IDF方案应用了单词/字符n元语法特征的各种组合。此外,我们应用了7种基本预处理方法的各种组合。我们最好的提交是RNN模型,在最复杂的子任务(C)的65个提交中排名第25。

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