BNU-HKBU UIC NLP Team 2 at SemEval-2019 Task 6: Detecting Offensive Language Using BERT model
Zhenghao Wu, Hao Zheng, Jianming Wang, Weifeng Su, Jefferson Fong
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In this study we deal with the problem of identifying and categorizing offensive language in social media. Our group, BNU-HKBU UIC NLP Team2, use supervised classification along with multiple version of data generated by different ways of pre-processing the data. We then use the state-of-the-art model Bidirectional Encoder Representations from Transformers, or BERT (Devlin et al, 2018), to capture linguistic, syntactic and semantic features. Long range dependencies between each part of a sentence can be captured by BERT's bidirectional encoder representations. Our results show 85.12\% accuracy and 80.57\% F1 scores in Subtask A (offensive language identification), 87.92\% accuracy and 50\% F1 scores in Subtask B (categorization of offense types), and 69.95\% accuracy and 50.47\% F1 score in Subtask C (offense target identification). Analysis of the results shows that distinguishing between targeted and untargeted offensive language is not a simple task. More work needs to be done on the unbalance data problem in Subtasks B and C. Some future work is also discussed.