Abstract
Graph based representations have been utilized to achieve state-of-the-art performance in text classification tasks. The same basic structure underlies knowledge graphs, large knowledge bases that contain rich information about the world. This paper capitalises on the graph of words model and enriches it with concepts from knowledge graphs, resulting in more powerful hybrid representations of a corpus. We focus on the domain of medical text classification and medical ontologies in order to test our proposed methods and analyze different alternatives in terms of text representation models and knowledge injection techniques. The method we present produces text representations that are both explainable and effective in improving the accuracy on the OHSUMED classification task, surpassing neural network architectures such as GraphStar and Text GCN.
We acknowledge support of this work by the project “APOLLONIS.” (MIS 5002738) which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014–2020) and co-financed by Greece and the European Union (European Regional Development Fund) (applicable for Alexios Mandalios, Alexandros Chortaras, Giorgos Stamou).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, B.: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2311–2320 (2018)
Yao, L., Mao, C., Luo, Y.: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7370–7377 (2019)
Wu, F., Zhang, T., de Souza Jr., A.H., Fifty, C., Yu, T., Weinberger, K.Q.: arXiv preprint arXiv:1902.07153 (2019)
Haonan, L., Huang, S.H., Ye, T., Xiuyan, G.: arXiv preprint arXiv:1906.12330 (2019)
Liu, Z., Chu, W.W.: Inf. Retrieval 10(2), 173 (2007)
Wang, J.Z., Zhang, Y., Dong, L., Li, L., Srimani, P.K., Philip, S.Y.: BMC Bioinform. 15(S12), S1 (2014)
Huang, L., Milne, D., Frank, E., Witten, I.H.: J. Am. Soc. Inf. Sci. Technol. 63(8), 1593 (2012)
Albitar, S., Espinasse, B., Fournier, S.: The Twenty-Seventh International Flairs Conference (2014)
Loper, E., Bird, S.: arXiv preprint cs/0205028 (2002)
Honnibal, M., Montani, I.: spaCy 2: natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing (2017, To appear)
Rousseau, F., Vazirgiannis, M.: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 59–68 (2013)
Donnelly, K.: Stud. Health Technol. Inform. 121, 279 (2006)
Webber, J.: Proceedings of the 3rd Annual Conference on Systems, Programming, and Applications: Software for Humanity, pp. 217–218 (2012)
Aronson, A.R.: Proceedings of the AMIA Symposium (American Medical Informatics Association 2001), p. 17 (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mandalios, A., Chortaras, A., Stamou, G., Vazirgiannis, M. (2021). Enriching Graph Representations of Text: Application to Medical Text Classification. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-65351-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-65351-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65350-7
Online ISBN: 978-3-030-65351-4
eBook Packages: EngineeringEngineering (R0)