  This paper describes the UM-IU@LING's system for the SemEval 2019 Task 6:
OffensEval. We take a mixed approach to identify and categorize hate speech in
social media. In subtask A, we fine-tuned a BERT based classifier to detect
abusive content in tweets, achieving a macro F1 score of 0.8136 on the test
data, thus reaching the 3rd rank out of 103 submissions. In subtasks B and C,
we used a linear SVM with selected character n-gram features. For subtask C,
our system could identify the target of abuse with a macro F1 score of 0.5243,
ranking it 27th out of 65 submissions.
