SemEval-2020 Task 12 was OffenseEval: Multilingual Offensive Language Identification in Social Media (Zampieri et al., 2020). The task was subdivided into multiple languages and datasets were provided for each one. The task was further divided into three sub-tasks: offensive language identification, automatic categorization of offense types, and offense target identification. I have participated in the task-C, that is, offense target identification. For preparing the proposed system, I have made use of Deep Learning networks like LSTMs and frameworks like Keras which combine the bag of words model with automatically generated sequence based features and manually extracted features from the given dataset. My system on training on 25% of the whole dataset achieves macro averaged f1 score of 47.763%.

翻译：SemEval-2020任务12是 " 防御:社会媒体中的多语言进攻性语言识别 " (Zampieri等人,2020年),该任务被细分为多种语言,每个任务都有数据集。该任务还被进一步分为三个子任务:攻击性语言识别、犯罪类型的自动分类和犯罪目标识别。我参加了任务-C,即犯罪目标识别。为准备拟议的系统,我利用了诸如LSTMs和Keras等框架的深学习网络,这些网络将一包文字模型与自动生成的序列特征和从给定数据集中手工提取的特征结合起来。我关于整个数据集中25%的培训系统实现了47.763%的宏观平均F1分。