论文标题
GHMERTI在Semeval-2019任务6：一种基于词语和角色的进攻语言识别方法
Ghmerti at SemEval-2019 Task 6: A Deep Word- and Character-based Approach to Offensive Language Identification
论文作者
论文摘要
本文介绍了GHMERTI团队在2019年Semeval共享任务的子任务A和B提交的模型。《进攻》解决了在三个子任务中识别和分类社交媒体中进攻性语言的问题；是否有攻击性（子任务A），是否针对个人，一个组或其他实体（子任务）（子任务C）（子任务C）。提出的方法包括角色级卷积神经网络，单词级复发性神经网络以及一些预处理。提议的子任务A所实现的性能为77.93％的宏观平均F1得分。
This paper presents the models submitted by Ghmerti team for subtasks A and B of the OffensEval shared task at SemEval 2019. OffensEval addresses the problem of identifying and categorizing offensive language in social media in three subtasks; whether or not a content is offensive (subtask A), whether it is targeted (subtask B) towards an individual, a group, or other entities (subtask C). The proposed approach includes character-level Convolutional Neural Network, word-level Recurrent Neural Network, and some preprocessing. The performance achieved by the proposed model for subtask A is 77.93% macro-averaged F1-score.
