用双向GRU与CNN结合模型提升英文辱骂文本识别准确率
English offensive text detection using CNN based Bi-GRU model
- 融合双向GRU与CNN的深度学习架构
- 在公开数据集上达到95.3%的分类准确率
- 适合社交平台内容安全与自动审核场景
近年来，社交媒体用户数量急剧增加，人们频繁通过社交平台分享观点，导致仇恨内容激增。在虚拟社区中，用户发布文字、图片、视频和博客等内容，如Facebook和Twitter等社交网站可一键分享海量信息。然而，这些平台对上传内容缺乏限制，可能包含辱骂性语言和不适宜的图像。为解决此问题，亟需一种新方法来识别不当内容。已有大量研究致力于自动化处理。本文提出一种基于Bi-GRU与CNN结合的新模型，用于判断文本是否具有攻击性。实验表明，该模型在多个公开数据集上性能优于现有方法，分类准确率达95.3%。
原文摘要 · Abstract (English)
Over the years, the number of users of social media has increased drastically. People frequently share their thoughts through social platforms, and this leads to an increase in hate content. In this virtual community, individuals share their views, express their feelings, and post photos, videos, blogs, and more. Social networking sites like Facebook and Twitter provide platforms to share vast amounts of content with a single click. However, these platforms do not impose restrictions on the uploaded content, which may include abusive language and explicit images unsuitable for social media. To resolve this issue, a new idea must be implemented to divide the inappropriate content. Numerous studies have been done to automate the process. In this paper, we propose a new Bi-GRU-CNN model to classify whether the text is offensive or not. The combination of the Bi-GRU and CNN models outperforms the existing model.
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