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2019
Conference Paper
Titel
FraunhoferSIT at GermEval 2019: Can Machines Distinguish Between Offensive Language and Hate Speech? Towards a Fine-Grained Classification
Abstract
In this paper, we describe the Fraunhofer-SIT submission for the ""GermEval 2019 - Shared Task on the Identification of Offensive Language"". We participated in two subtasks: task 1 is a binary classification of German tweets on the identification of offensive language. Task 2 is a fine-grained classification to distinguish between three subcategories of offensive language. Our best model is an SVM classifier based on tfidf character n-gram features. Our submitted runs in the shared task are: Fraunhofer-SIT coarse [1-3].txt for task 1 and FraunhoferSIT fine [1-3].txt for task 2. Our final system reaches 0.70 macro-average F1-score for the binary classification and 0.46 F1-score for the fine-grained classification. The achieved results show that the problem of automatically distinguishing between offensive language and ""Hate Speech"" is far from being solved.