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Paper Abstract and Keywords
Presentation 2024-09-12 09:00
Relation extraction using a combination of multi-task learning and task-specific large language models
Tomokazu Hayashi, Hisashi Miyamori (Kyoto Sangyo Univ.) DE2024-5
Abstract (in Japanese) (See Japanese page) 
(in English) Relation extraction, which extracts relations between entities from text, is important because it leads to the construction of knowledge graphs and the discovery of problems and solutions to various events. Previous research has proposed a method that performs the tasks of entity recognition and relation extraction using multitask learning, and it has shown higher performance than existing methods. However, this method is affected by the equilibrium between the number of shared layers and task-specific layers, and does not necessarily show sufficient performance in the relation extraction task. In this paper, we propose a relation extraction method that connects the final hidden layer of a task-specific large language model to the shared layer of multi-task learning, focusing on the superiority of expressions in large language models. In experiments, we verify the usefulness of the proposed method by comparing its performance with that of a multitask learning method using a model specialized for recognition of unique expressions in combination with a method using a task-specific large language model.
Keyword (in Japanese) (See Japanese page) 
(in English) Multi-task Learning / Large Language Models / Relation Extraction / Named Entity Recognition / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 185, DE2024-5, pp. 7-12, Sept. 2024.
Paper # DE2024-5 
Date of Issue 2024-09-04 (DE) 
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 DE2024-5

Conference Information
Committee DE IPSJ-DBS IPSJ-IFAT  
Conference Date 2024-09-11 - 2024-09-12 
Place (in Japanese) (See Japanese page) 
Place (in English) Awaji Yumebutai 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Bigdata management, information retrieval, knowledge discovery, etc. 
Paper Information
Registration To DE 
Conference Code 2024-09-DE-DBS-IFAT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Relation extraction using a combination of multi-task learning and task-specific large language models 
Sub Title (in English)  
Keyword(1) Multi-task Learning  
Keyword(2) Large Language Models  
Keyword(3) Relation Extraction  
Keyword(4) Named Entity Recognition  
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1st Author's Name Tomokazu Hayashi  
1st Author's Affiliation Kyoto Sangyo University (Kyoto Sangyo Univ.)
2nd Author's Name Hisashi Miyamori  
2nd Author's Affiliation Kyoto Sangyo University (Kyoto Sangyo Univ.)
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Speaker Author-1 
Date Time 2024-09-12 09:00:00 
Presentation Time 25 minutes 
Registration for DE 
Paper # DE2024-5 
Volume (vol) vol.124 
Number (no) no.185 
Page pp.7-12 
#Pages 6 
Date of Issue 2024-09-04 (DE) 

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