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Simon @ DravidianLangTech-EACL2021: Meme Classification for Tamil with BERT

机译:Simon @ Dravidianlangtech-EACL2021:MEME与BERT的泰米尔的分类

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In this paper, we introduce the system for the task of meme classification for Tamil, submitted by our team. In today's society, social media has become an important platform for people to communicate. We use social media to share information about ourselves and express our views on things. It has gradually developed a unique form of emotional expression on social media - meme. The meme is an expression that is often ironic. This also gives the meme a unique sense of humor. But it's not just positive content on social media. There's also a lot of offensive content. Meme's unique expression makes it often used by some users to post offensive content. Therefore, it is very urgent to detect the offensive content of the meme. Our team uses the natural language processing method to classify the offensive content of the meme. Our team combines the BERT model with the CNN to improve the model's ability to collect statement information. Finally, the F1-score of our team in the official test set is 0.49. and our method ranks 5th.
机译:在本文中,我们介绍了我们团队提交的泰米尔MEME分类任务的系统。在今天的社会中,社会媒体已成为沟通的重要平台。我们使用社交媒体分享有关自己的信息,并表达对事物的看法。它在社交媒体上逐渐发展了一种独特的情感表达形式 - MEME。 MEME是一种通常是讽刺的表达。这也给了模因幽默感。但这不仅仅是社交媒体的积极内容。还有很多令人反感的内容。 MEME的独特表达使某些用户经常用于发布攻击内容。因此,检测MEME的令人反感含量非常迫切。我们的团队使用自然语言处理方法来分类MEME的令人反感。我们的团队将BERT模型与CNN结合,以改善模型收集声明信息的能力。最后,在官方测试集中团队的F1分数为0.49。我们的方法排名第5。

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