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This paper describes the Duluth systems that participated in SemEval--2019Task 6, Identifying and Categorizing Offensive Language in Social Media(OffensEval). For the most part these systems took traditional Machine Learningapproaches that built classifiers from lexical features found in manuallylabeled training data. However, our most successful system for classifying atweet as offensive (or not) was a rule-based black--list approach, and we alsoexperimented with combining the training data from two different but relatedSemEval tasks. Our best systems in each of the three OffensEval tasks placed inthe middle of the comparative evaluation, ranking 57th of 103 in task A, 39thof 75 in task B, and 44th of 65 in task C.

Duluth at SemEval-2019 Task 6: Lexical Approaches to Identify and Categorize Offensive Tweets