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        <title>Hierarchical Multi-label Classification of Text with Capsule Networks</title>
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    <abstract>Capsule networks have been shown to demonstrate good performance on structured data in the area of visual inference. In this paper we apply and compare simple shallow capsule networks for hierarchical multi-label text classification and show that they can perform superior to other neural networks, such as CNNs and LSTMs, and non-neural network architectures such as SVMs. For our experiments, we use the established Web of Science (WOS) dataset and introduce a new real-world scenario dataset, the BlurbGenreCollection (BGC). Our results confirm the hypothesis that capsule networks are especially advantageous for rare events and structurally diverse categories, which we attribute to their ability to combine latent encoded information.</abstract>
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    <identifier type="doi">10.18653/v1/P19-2045</identifier>
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%0 Conference Proceedings
%T Hierarchical Multi-label Classification of Text with Capsule Networks
%A Aly, Rami
%A Remus, Steffen
%A Biemann, Chris
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F aly-etal-2019-hierarchical
%X Capsule networks have been shown to demonstrate good performance on structured data in the area of visual inference. In this paper we apply and compare simple shallow capsule networks for hierarchical multi-label text classification and show that they can perform superior to other neural networks, such as CNNs and LSTMs, and non-neural network architectures such as SVMs. For our experiments, we use the established Web of Science (WOS) dataset and introduce a new real-world scenario dataset, the BlurbGenreCollection (BGC). Our results confirm the hypothesis that capsule networks are especially advantageous for rare events and structurally diverse categories, which we attribute to their ability to combine latent encoded information.
%R 10.18653/v1/P19-2045
%U https://aclanthology.org/P19-2045
%U https://doi.org/10.18653/v1/P19-2045
%P 323-330

Markdown (Informal)

[Hierarchical Multi-label Classification of Text with Capsule Networks](https://aclanthology.org/P19-2045) (Aly et al., ACL 2019)

• Hierarchical Multi-label Classification of Text with Capsule Networks (Aly et al., ACL 2019)

ACL

• Rami Aly, Steffen Remus, and Chris Biemann. 2019. Hierarchical Multi-label Classification of Text with Capsule Networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 323–330, Florence, Italy. Association for Computational Linguistics.