An Analysis of BERT (NLP) for Assisted Subject Indexing for Project Gutenberg. Issue 8 (17th November 2022)

• Record Type: Journal Article
• Authors: Chou, Charlene; Chu, Tony
• Is Part Of: Cataloging & classification quarterly. Volume 60:Issue 8(2022)
• Page Start: 807
• Page End: 835
• Publication Date: 2022-11-17
• Subjects: Automatic subject indexing -- NLP (natural language processing) -- BERT (Bidirectional Encoder Representations from Transformers) -- Library of Congress Subject Headings (LCSH) -- Library of Congress Classification (LCC) -- AI (artificial intelligence) -- library metadata; Cataloging -- Periodicals; Classification -- Books -- Periodicals; 025.3
• DOI: 10.1080/01639374.2022.2138666
• Languages: English
• ISSNs: 0163-9374

Abstract

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.