UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs
Table 5: Results on the trial data for subtask C.
['[BOLD] System', '[BOLD] F11 macro', '[BOLD] Acc.']
Since there are only 31 instances in the trial set and it is rather imbalanced, we can see that the highest accuracy is reached by classifying all examples as IND, i.e., the all IND baseline. Even though none of the classifiers outperformed the baseline in terms of accuracy, all the classifiers achieved significantly higher macro F1 scores, which shows that they are better at identifying the other two classes. After adding linguistic and emoji features, the character n-gram model showed a slight improvement in macro F1 score and achieved the highest accuracy along with the simple character n-gram model. But both macro F1 and accuracy dropped when entity information was added.