Document (#44141)

Author
Chou, C.
Chu, T.
Title
¬An analysis of BERT (NLP) for assisted subject indexing for Project Gutenberg
Source
Cataloging and classification quarterly. 60(2022) no.8, p.807-835
Year
2022
Abstract
In light of AI (Artificial Intelligence) and NLP (Natural language processing) technologies, this article examines the feasibility of using AI/NLP models to enhance the subject indexing of digital resources. While BERT (Bidirectional Encoder Representations from Transformers) models are widely used in scholarly communities, the authors assess whether BERT models can be used in machine-assisted indexing in the Project Gutenberg collection, through suggesting Library of Congress subject headings filtered by certain Library of Congress Classification subclass labels. The findings of this study are informative for further research on BERT models to assist with automatic subject indexing for digital library collections.
Content
Vgl.: https://www.tandfonline.com/doi/full/10.1080/01639374.2022.2138666.
Theme
Computerlinguistik
Automatisches Indexieren
Object
BERT
Projekt Gutenberg
LCSH
LCC

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