jhan014 at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media
Jiahui Han, Shengtan Wu, Xinyu Liu · 2019
In this paper, the team jhan014 presents two methods to identify and categorize the offensive language in Twitter.In the first method, we develop a deep neural network consisting of bidirectional recurrent layers with Gated Recurrent Unit (GRU) cells and fully connected layers.In the second method, we establish a probabilistic model, modified sentence offensiveness calculation (MSOC) to evaluate the sentence offensiveness level and target level according to different sub-tasks.Based on task results, We evaluate the performance of each method based on F1 score and analyze the advantages and disadvantages of these two methods with the type I error and type II error.In conclusion, deep neural network behaves well in all subtasks but has more type I error and fails to categorize subclasses with minor data or less character, while MSOC model does better in target categorizing but has more type II error in offensive identifying.