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Supporting Deep Learning-Based Named Entity Recognition Using Cloud Resource Management

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HCI International 2023 – Late Breaking Papers (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14059))

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Abstract

This paper presents a system for managing Cloud Resources such as memory and CPU/GPU that is used to develop, train, and customize Deep Learning-based Named Entity Recognition (NER) models in domains like heath care. The increasing digitization of healthcare services has led to the emergence of electronic health records (EHRs) as a significant component of healthcare data management. NER is a machine learning technique that can be applied to EHRs to extract information such as drug and treatment information, helping to support clinical decision making. The paper is addressing the difficulty domain experts face in using Cloud technologies to perform NER tasks, since they often require technical expertise and technical management overhead. The paper presents a system for the configuration of cloud resources for NER training using the spaCy framework and AWS compute services. The research is structured using Nunamaker’s methodology, which provides a structured approach to software development through four phases: observation, theory building, systems development, and experimentation. The paper identifies problem statements and research questions to guide the research and maps them to the objectives of the methodology. The objectives of the methodology include researching the state-of-the-art of NER and cloud technologies, analyzing the architecture of motivating research projects, defining user requirements and the system architecture, and implementing the system. The system is designed using User Centered Systems Design and is based on previously identified user requirements. Two main user groups are considered for the application: NER Experts and Medical Domain Experts. The system is implemented using the Model-View-Controller architecture pattern. It allows for the training of Transformer models, selection of compute resources, and adjusting training configuration and hyperparameters. The system is designed for scalability of compute and storage resources. The paper also discusses the evaluation of the system through experiments and analysis of the results to gain insights. It provides information about the technical implementation and details about the user interface. It is evaluated using cognitive walkthrough and experiments with Transformer-based models.

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Notes

  1. 1.

    https://cordis.europa.eu/programme/id/H2020_DT-ICT-12-2020.

  2. 2.

    https://www.ftk.de/en.

  3. 3.

    https://spacy.io/.

  4. 4.

    https://aws.amazon.com.

  5. 5.

    https://paperswithcode.com/sota/named-entity-recognition-ner-on-conll-2003.

  6. 6.

    https://aws.amazon.com.

  7. 7.

    https://azure.com.

  8. 8.

    https://cloud.google.com.

  9. 9.

    https://aws.amazon.com/pricing.

  10. 10.

    https://www.spacy.io.

  11. 11.

    https://www.python.org.

  12. 12.

    https://flask.palletsprojects.com/en/2.2.x/.

  13. 13.

    https://www.getbootstrap.com.

  14. 14.

    https://boto3.amazonaws.com/v1/documentation/api/latest/index.html.

  15. 15.

    https://huggingface.co.

  16. 16.

    https://aws.amazon.com/batch/.

  17. 17.

    https://docker.io.

  18. 18.

    https://kubernetes.io/.

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Acknowledgements

The author, Benedict Hartmann, acknowledges the financial support provided by Allianz Technology SE to attend HCI International 2023.

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Authors and Affiliations

  1. University of Hagen, Universitätsstr. 11, 58084, Hagen, Germany

    Benedict Hartmann, Philippe Tamla & Matthias Hemmje

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  1. Benedict Hartmann
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Correspondence to Benedict Hartmann .

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Editors and Affiliations

  1. Siemens Corporation, Princeton, NJ, USA

    Helmut Degen

  2. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

  3. San Jose State University, San Jose, CA, USA

    Abbas Moallem

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Hartmann, B., Tamla, P., Hemmje, M. (2023). Supporting Deep Learning-Based Named Entity Recognition Using Cloud Resource Management. In: Degen, H., Ntoa, S., Moallem, A. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14059. Springer, Cham. https://doi.org/10.1007/978-3-031-48057-7_6

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  • DOI: https://doi.org/10.1007/978-3-031-48057-7_6

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