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Automatically Detecting References from the Scholarly Literature to Records in Archives

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Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration (ICADL 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14458))

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Abstract

Scholars use references in books and articles to materials found in archives as one way of finding those materials, but present systems for archival access do not exploit that information. To change that, the first step is to find archival references in the scholarly literature; that is the focus of this paper. Several classifier designs are compared using a few thousand manually annotated footnotes and endnotes assembled from a large set of open access papers on history. The results indicate that fairly high recall and precision can be achieved.

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Notes

  1. 1.

    https://www.semanticscholar.org/product/api.

  2. 2.

    https://github.com/tokinori8/archive-citation-collection.

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Acknowledgments

This work was supported by JSPS KAKENHI Grant Number JP23KK0005.

Author information

Authors and Affiliations

  1. Kyushu University, Fukuoka, Japan

    Tokinori Suzuki, Emi Ishita & Yoichi Tomiura

  2. University of Maryland, College Park, MD, USA

    Douglas W. Oard

Authors
  1. Tokinori Suzuki
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  2. Douglas W. Oard
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  3. Emi Ishita
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  4. Yoichi Tomiura
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Corresponding author

Correspondence to Tokinori Suzuki .

Editor information

Editors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Dion H. Goh

  2. Academia Sinica, Taipei, Taiwan

    Shu-Jiun Chen

  3. Mahidol University, Tambon Salaya, Amphoe Phutthamonthon, Thailand

    Suppawong Tuarob

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Suzuki, T., Oard, D.W., Ishita, E., Tomiura, Y. (2023). Automatically Detecting References from the Scholarly Literature to Records in Archives. In: Goh, D.H., Chen, SJ., Tuarob, S. (eds) Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration. ICADL 2023. Lecture Notes in Computer Science, vol 14458. Springer, Singapore. https://doi.org/10.1007/978-981-99-8088-8_9

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  • DOI: https://doi.org/10.1007/978-981-99-8088-8_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8087-1

  • Online ISBN: 978-981-99-8088-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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