Scmhl5 at TRAC-2 Shared Task on Aggression Identification: Bert Based Ensemble Learning Approach
Scmhl5 at TRAC-2 Shared Task on Aggression Identification: Bert Based Ensemble Learning Approach
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发表时间：
2020-05
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通讯作者：
Han Liu;P. Burnap;Wafa Alorainy;M. Williams
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作者：
Han Liu;P. Burnap;Wafa Alorainy;M. Williams
本文介绍了一个系统开发过程中，我们的参与（团队名称：scmhl 5）在TRAC-2共享任务的攻击识别。特别是，我们参加了英语子任务A的三级分类（“公开攻击”，“隐性攻击”和“非攻击”）和英语子任务B的二元分类厌女症的侵略（“性别”或“非性别”）。对于这两个子任务，我们的方法涉及使用预训练的Bert模型将每个实例的文本提取到768维嵌入向量中，然后在嵌入特征上训练分类器集合。对于子任务A，我们的方法获得了0.703的准确度和0.664的加权F-测量，而对于子任务B，准确度为0.869，加权F-测量为0.851。就排名而言，使用我们的方法获得的子任务A的加权F-度量在16个团队中排名第10，而对于子任务B，加权F-度量在15个团队中排名第8。
This paper presents a system developed during our participation (team name: scmhl5) in the TRAC-2 Shared Task on aggression identification. In particular, we participated in English Sub-task A on three-class classification (‘Overtly Aggressive’, ‘Covertly Aggressive’ and ‘Non-aggressive’) and English Sub-task B on binary classification for Misogynistic Aggression (‘gendered’ or ‘non-gendered’). For both sub-tasks, our method involves using the pre-trained Bert model for extracting the text of each instance into a 768-dimensional vector of embeddings, and then training an ensemble of classifiers on the embedding features. Our method obtained accuracy of 0.703 and weighted F-measure of 0.664 for Sub-task A, whereas for Sub-task B the accuracy was 0.869 and weighted F-measure was 0.851. In terms of the rankings, the weighted F-measure obtained using our method for Sub-task A is ranked in the 10th out of 16 teams, whereas for Sub-task B the weighted F-measure is ranked in the 8th out of 15 teams.