<p>Predicting biology-related library of congress subject headings for scholarly articles</p>

Nazmul Kazi; Nathaniel Lane; Indika Kahanda

doi:10.7490/f1000research.1118336.1

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Kazi N, Lane N and Kahanda I. Predicting biology-related library of congress subject headings for scholarly articles [version 1; not peer reviewed]. F1000Research 2020, 9(ISCB Comm J):1214 (poster) (https://doi.org/10.7490/f1000research.1118336.1)

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Predicting biology-related library of congress subject headings for scholarly articles

Nazmul Kazi¹, Nathaniel Lane, Indika Kahanda²

Published 09 Oct 2020 (https://doi.org/10.7490/f1000research.1118336.1)

Author Affiliations

¹ Montana State University, Bozeman, USA
² University of North Florida, USA

Copyright © 2020 Kazi N et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Published 09 Oct 2020

Predicting biology-related library of congress subject headings for scholarly articles
[version 1; not peer reviewed]

Nazmul Kazi¹, Nathaniel Lane, Indika Kahanda²

Author Affiliations

¹ Montana State University, Bozeman, USA
² University of North Florida, USA

Presented at

ISMB 2020

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Library of Congress Subject Headings, LCSH, IR, Repository Analytics and Metrics Portal, RAMP, Biology, Text mining, multi-label classification, ANN, Decision Tree, NN, Neural Network

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