With the rising number of Internet users, there has been a rapid increase in cyberbullying. Among the types of cyberbullying, verbal abuse is emerging as the most serious problem, for preventing which profanity is being identified and blocked. However, users employ words cleverly to avoid blocking. With the existing profanity discrimination methods, deliberate typos and profanity using special characters can be discriminated with high accuracy. However, as they cannot grasp the meaning of the words and the flow of sentences, standard words such as “Sibaljeom (starting point, a Korean word that sounds similar to a swear word)” and “Saekkibalgalag (little toe, a Korean word that sounds similar to another swear word)” are less accurately discriminated. Therefore, in order to solve this problem, this study proposes a method of discriminating profanity using a deep learning model that can grasp the meaning and context of words after separating Hangul into the onset, nucleus, and coda.

中文翻译：

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使用词嵌入和LSTM进行亵渎检测的方法

随着互联网用户数量的增加，网络欺凌行为迅速增加。在网络欺凌类型中，言语虐待正成为最严重的问题，用于防止识别和阻止亵渎行为。然而，用户巧妙地使用单词以避免阻塞。利用现有的亵渎辨别方法，可以高精度地区分使用特殊字符的故意错别字和亵渎行为。但是，由于他们无法掌握单词的含义和句子的流程，因此使用了标准单词，例如“ Sibaljeom（起点，听起来像是脏话的朝鲜语单词）”和“ Saekkibalgalag（小脚趾，听起来与另一个咒骂字词相似”）。因此，为了解决这个问题，