An Analysis of BERT (NLP) for Assisted Subject Indexing for Project Gutenberg

• Published in: Cataloging & classification quarterly Vol. 60; no. 8; pp. 807 - 835
• Main Authors: Chou, Charlene, Chu, Tony
• Format: Journal Article
• Language: English
• Published: New York Routledge 17.11.2022; Taylor & Francis Ltd
• Subjects: AI (artificial intelligence); Artificial intelligence; Automatic subject indexing; BERT (Bidirectional Encoder Representations from Transformers); library metadata; Library of Congress Classification (LCC); Library of Congress Subject Headings (LCSH); Natural language processing; NLP (natural language processing); Subject indexing
• ISSN: 0163-9374; 1544-4554
• DOI: 10.1080/01639374.2022.2138666

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.