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Paper Abstract and Keywords
Presentation 2022-05-27 13:25
Improvement of Performance of Question and Answering System using Ontology Generation
Ayato Kuwana, Incheon Paik (UoA) SC2022-7
Abstract (in Japanese) (See Japanese page) 
(in English) Automating ontology generation from raw text corpus is required to meet the ontology demand. As an initial attempt of ontology generation with a neural network, a recurrent neural network (RNN)-based method is proposed. However, updating the architecture is possible because of the development in natural language processing (NLP). In contrast, the transfer learning of language models trained by a large unlabeled corpus such as bidirectional encoder representations from transformers (BERT) has yielded a breakthrough in NLP. Inspired by these achievements, to apply transfer learning of language models, we propose a novel workflow for ontology generation consisting of two-stage learning. This paper provides a quantitative comparison between the proposed method and the existing methods. Our result showed that our best method improved accuracy by over 12.5%. To show an application example, we applied our model to Stanford Question Answering Dataset (SQuAD) dataset to show ontology generation in a real field. The result shows our model can generate good ontology with some exceptions that requests future research for improving the ontology quality.
Keyword (in Japanese) (See Japanese page) 
(in English) Ontology / Automation of Generation / Deep Pretrained Model / Question and Answering System / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 50, SC2022-7, pp. 37-42, May 2022.
Paper # SC2022-7 
Date of Issue 2022-05-20 (SC) 
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 SC2022-7

Conference Information
Committee SC  
Conference Date 2022-05-27 - 2022-05-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) AI Service and Digital Transformation, and general topics 
Paper Information
Registration To SC 
Conference Code 2022-05-SC 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Improvement of Performance of Question and Answering System using Ontology Generation 
Sub Title (in English)  
Keyword(1) Ontology  
Keyword(2) Automation of Generation  
Keyword(3) Deep Pretrained Model  
Keyword(4) Question and Answering System  
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1st Author's Name Ayato Kuwana  
1st Author's Affiliation University of Aizu (UoA)
2nd Author's Name Incheon Paik  
2nd Author's Affiliation University of Aizu (UoA)
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Speaker Author-2 
Date Time 2022-05-27 13:25:00 
Presentation Time 25 minutes 
Registration for SC 
Paper # SC2022-7 
Volume (vol) vol.122 
Number (no) no.50 
Page pp.37-42 
#Pages 6 
Date of Issue 2022-05-20 (SC) 

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