Abstract
A huge number of papers have been published about COVID-19. So much it’s overwhelming. Many papers appear on preprint servers such as arXiv before publication. Researchers and clinicians can get ahead of the curve by making use of these preprint papers, but how to tell what is worth reading? Could there be an automated recommendation mechanism? In this paper we address the question by experimenting with SPECTER document-level vector embedding which establishes the representations by incorporating state-of-the-art Transformer models, such as SciBERT, a BERT variant tailored to scientific text. Meanwhile, the dataset we choose to apply SPECTER embedding is the CORD-19 dataset.
This work was supported by JST (JPMJMS2033). The last author would like to thank Advanced Telecommunications Research Institute for his research visit there.
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Acknowledgment
The authors are grateful to Ryohei Sasano for his help with the experimental part of this work.
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Xu, T., Hinton, N., Bennett, M.T., Maruyama, Y. (2022). Natural Language Processing for Scientific Paper Evaluation: Comparing Human and Machine Judgements. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2022 – Late Breaking Posters. HCII 2022. Communications in Computer and Information Science, vol 1655. Springer, Cham. https://doi.org/10.1007/978-3-031-19682-9_90
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DOI: https://doi.org/10.1007/978-3-031-19682-9_90
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