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European Conference on Information Retrieval

ECIR 2021: Advances in Information Retrieval pp 559–564Cite as

Brief Description of COVID-SEE: The Scientific Evidence Explorer for COVID-19 Related Research

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12657))

Abstract

We present COVID-SEE, a system for medical literature discovery based on the concept of information exploration, which builds on several distinct text analysis and natural language processing methods to structure and organise information in publications, and augments search through a visual overview of a collection enabling exploration to identify key articles of interest. We developed this system over COVID-19 literature to help medical professionals and researchers explore the literature evidence, and improve findability of relevant information. COVID-SEE is available at http://covid-see.com.

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Acknowledgements

This research was conducted by the Australian Research Council Training Centre in Cognitive Computing for Medical Technologies (project number ICI70200030) and funded by the Australian Government.

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Authors and Affiliations

  1. RMIT University, Melbourne, Australia

    Karin Verspoor

  2. The University of Melbourne, Melbourne, Australia

    Karin Verspoor, Simon Šuster, Yulia Otmakhova, Shevon Mendis, Zenan Zhai, Biaoyan Fang, Jey Han Lau, Timothy Baldwin, Antonio Jimeno Yepes & David Martinez

  3. IBM Research Australia, Carlton, Australia

    Yulia Otmakhova, Antonio Jimeno Yepes & David Martinez

Authors
  1. Karin Verspoor
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  2. Simon Šuster
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  3. Yulia Otmakhova
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  4. Shevon Mendis
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  5. Zenan Zhai
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  6. Biaoyan Fang
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  7. Jey Han Lau
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  8. Timothy Baldwin
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  9. Antonio Jimeno Yepes
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  10. David Martinez
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Corresponding author

Correspondence to Karin Verspoor .

Editor information

Editors and Affiliations

  1. Radboud University Nijmegen, Nijmegen, The Netherlands

    Djoerd Hiemstra

  2. Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium

    Marie-Francine Moens

  3. Toulouse, Toulouse Institute of Computer Science Research, Toulouse, France

    Josiane Mothe

  4. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Raffaele Perego

  5. Leipzig University, Leipzig, Germany

    Martin Potthast

  6. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Fabrizio Sebastiani

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Verspoor, K. et al. (2021). Brief Description of COVID-SEE: The Scientific Evidence Explorer for COVID-19 Related Research. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_65

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  • DOI: https://doi.org/10.1007/978-3-030-72240-1_65

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72239-5

  • Online ISBN: 978-3-030-72240-1

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