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Federated Learning in Named Entity Recognition

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Recent Trends in Analysis of Images, Social Networks and Texts (AIST 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1357))

Abstract

This article is devoted to the implementation of the federated approach to named entity recognition. The novel federated approach is designed to solve data privacy issues. The classic BiLSTM-CNNs-CRF and its modifications trained on a single machine are taken as baseline. Federated training is conducted for them. Influence of use of pretrained embedding, use of various blocks of architecture on training and quality of final model is considered. Besides, other important questions arising in practice are considered and solved, for example, creation of distributed private dictionaries, selection of base model for federated learning.

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Acknowledgments

The research was supported by the Russian Science Foundation grant 19-11-00281.

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Correspondence to Efim Luboshnikov or Ilya Makarov .

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Luboshnikov, E., Makarov, I. (2021). Federated Learning in Named Entity Recognition. In: van der Aalst, W.M.P., et al. Recent Trends in Analysis of Images, Social Networks and Texts. AIST 2020. Communications in Computer and Information Science, vol 1357. Springer, Cham. https://doi.org/10.1007/978-3-030-71214-3_8

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  • DOI: https://doi.org/10.1007/978-3-030-71214-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71213-6

  • Online ISBN: 978-3-030-71214-3

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