Tillmann Dönicke
Institute for Natural Language Processing, University of Stuttgart, Stuttgart, Germany
Florian Lux
Institute for Natural Language Processing, University of Stuttgart, Stuttgart, Germany
Matthias Damaschk
Institute for Natural Language Processing, University of Stuttgart, Stuttgart, Germany
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Published in: DL4NLP 2019. Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing, 30 September, 2019, University of Turku, Turku, Finland
Linköping Electronic Conference Proceedings 163:7, p. 58-65
NEALT Proceedings Series 38:7, p. 58-65
Published: 2019-09-27
ISBN: 978-91-7929-999-6
ISSN: 1650-3686 (print), 1650-3740 (online)
This paper discusses methods to improve the performance of text classification on data that is difficult to classify due to a large number of unbalanced classes with noisy examples. A variety of features are tested, in combination with three different neural-network-based methods with increasing complexity. The classifiers are applied to a songtext–artist dataset which is large, unbalanced and noisy. We come to the conclusion that substantial improvement can be obtained by removing unbalancedness and sparsity from the data. This fulfills a classification task unsatisfactorily—however, with contemporary methods, it is a practical step towards fairly satisfactory results.
