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The paper presents a system developed for the SemEval-2019 competition Task 5hat-Eval Basile et al. (2019) (team name: LU Team) and Task 6 OffensEvalZampieri et al. (2019b) (team name: NLPR@SRPOL), where we achieved 2nd positionin Subtask C. The system combines in an ensemble several models (LSTM,Transformer, OpenAI's GPT, Random forest, SVM) with various embeddings (custom,ELMo, fastText, Universal Encoder) together with additional linguistic features(number of blacklisted words, special characters, etc.). The system works witha multi-tier blacklist and a large corpus of crawled data, annotated forgeneral offensiveness. In the paper we do an extensive analysis of our resultsand show how the combination of features and embedding affect the performanceof the models.

NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier