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            <namePart type="given">Joakim</namePart>
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    <abstract>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 fulfils a classification task unsatisfactorily—however, with contemporary methods, it is a practical step towards fairly satisfactory results.</abstract>
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%0 Conference Proceedings
%T Multiclass Text Classification on Unbalanced, Sparse and Noisy Data
%A Dönicke, Tillmann
%A Damaschk, Matthias
%A Lux, Florian
%Y Nivre, Joakim
%Y Derczynski, Leon
%Y Ginter, Filip
%Y Lindi, Bjørn
%Y Oepen, Stephan
%Y Søgaard, Anders
%Y Tidemann, Jörg
%S Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing
%D 2019
%8 September
%I Linköping University Electronic Press
%C Turku, Finland
%F damaschk-etal-2019-multiclass
%X 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 fulfils a classification task unsatisfactorily—however, with contemporary methods, it is a practical step towards fairly satisfactory results.
%U https://aclanthology.org/W19-6207
%P 58-65

Markdown (Informal)

[Multiclass Text Classification on Unbalanced, Sparse and Noisy Data](https://aclanthology.org/W19-6207) (Dönicke et al., NoDaLiDa 2019)

• Multiclass Text Classification on Unbalanced, Sparse and Noisy Data (Dönicke et al., NoDaLiDa 2019)

ACL

• Tillmann Dönicke, Matthias Damaschk, and Florian Lux. 2019. Multiclass Text Classification on Unbalanced, Sparse and Noisy Data. In Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing, pages 58–65, Turku, Finland. Linköping University Electronic Press.