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Paper Abstract and Keywords
Presentation 2026-03-01 13:25
Automatic Prediction of Nippon Decimal Classification (NDC) Codes from Queries Using Large Language Models
Yuzuha Hayashi (MWU), Shuntaro Yada (Tsukuba), Noriyuki Okumura (MWU), Takuma Asaishi (Nanzan) NLC2025-17
Abstract (in Japanese) (See Japanese page) 
(in English) In school libraries, the proliferation of inquiry-based learning has heightened the demand for the rapid provision of instructional materials tailored to diverse lesson units and themes. However, the manual curation of "unit–NDC mapping tables" in the existing BookReach system poses significant challenges regarding classification accuracy and scalability. This study aims to optimize an automatic classification engine that predicts Nippon Decimal Classification (NDC) codes from arbitrary keywords. We conducted a comparative analysis of seven methods, including NDC Predictor, Word2Vec, BERT, ModernBERT, and GPT-4o. The evaluation utilized a dataset of 654 keyword–NDC pairs derived from junior high school inquiry projects, focusing on three-digit classification accuracy. The results indicate that ModernBERT (30M) achieved the highest individual performance with an accuracy of 88.53%. Furthermore, an ensemble approach utilizing a majority-vote mechanism reached an accuracy of 96.18%. These findings demonstrate that contemporary context-aware pretrained language models significantly outperform conventional methodologies. This research highlights the efficacy and practical potential of LLM-based automatic classification in enhancing instructional support within school library environments.
Keyword (in Japanese) (See Japanese page) 
(in English) School libraries / Nippon Decimal Classification (NDC) / Automatic classification / Large Language Models / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 366, NLC2025-17, pp. 28-33, Feb. 2026.
Paper # NLC2025-17 
Date of Issue 2026-02-21 (NLC) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NLC2025-17

Conference Information
Committee NLC  
Conference Date 2026-02-28 - 2026-03-01 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To NLC 
Conference Code 2026-03-NLC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Automatic Prediction of Nippon Decimal Classification (NDC) Codes from Queries Using Large Language Models 
Sub Title (in English)  
Keyword(1) School libraries  
Keyword(2) Nippon Decimal Classification (NDC)  
Keyword(3) Automatic classification  
Keyword(4) Large Language Models  
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1st Author's Name Yuzuha Hayashi  
1st Author's Affiliation Mukogawa Women's University (MWU)
2nd Author's Name Shuntaro Yada  
2nd Author's Affiliation University of Tsukuba (Tsukuba)
3rd Author's Name Noriyuki Okumura  
3rd Author's Affiliation Mukogawa Women's University (MWU)
4th Author's Name Takuma Asaishi  
4th Author's Affiliation Nanzan University (Nanzan)
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Speaker Author-1 
Date Time 2026-03-01 13:25:00 
Presentation Time 25 minutes 
Registration for NLC 
Paper # NLC2025-17 
Volume (vol) vol.125 
Number (no) no.366 
Page pp.28-33 
#Pages 6 
Date of Issue 2026-02-21 (NLC) 

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