Abstract
This paper deals with multi-label document classification using neural networks. We propose a novel neural network which is composed of two sub-nets: the first one estimates the scores for all classes, while the second one determines the number of classes assigned to the document. The proposed approach is evaluated on Czech and English standard corpora. The experimental results show that the proposed method is competitive with state of the art on both languages.
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Notes
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This approach has been proposed in [7].
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Acknowledgements
This work has been supported by the project LO1506 of the Czech Ministry of Education, Youth and Sports.
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Lenc, L., Král, P. (2017). Two-Level Neural Network for Multi-label Document Classification. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_42
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DOI: https://doi.org/10.1007/978-3-319-68612-7_42
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