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Multi-Label Classification of Blurbs with SVM Classifier Chains

Due to the increasing digitalisation multi-label classification gains in importance in many areas. In this paper we propose a method to classify blurbs into eight basic book genre using an ensemble of classifier chains composed of radial support vector machines using word embeddings and author information as features. Five models were tested using different implementations and features, as well as different numbers of chains. The best model reached a performance of a micro average F1 of 0.841.