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Paper Abstract and Keywords
Presentation 2024-09-02 13:30
Extraction of sentences containing surprising business in companies
Yoshimasa Kobayashi, Hiroyuki Sakai (SEIKEI Univ.), Kengo Enami (Tsuda Univ.) NLC2024-2
Abstract (in Japanese) (See Japanese page) 
(in English) In this research, we propose a method for estimating surprising businesses that are not generally known from the business description sentences of companies using a deep learning model and extracting sentences containing such businesses description. Our method automatically generates the training data for estimating surprising businesses by ranking the business sentences based on the similarity between the business sentences in the annual securities reports and the main business sentences of the company. Then, we construct a T5 model to estimate whether the business is a mainstay business or a surprising business by using the training data and our method extracts sentences containing surprising businesses by the T5 model. In the evaluation experiment, we manually evaluated sentences containing surprising business extracted by our method. As a result, 129 of 175 sentences were judged to be appropriately as sentences containing surprising business.
Keyword (in Japanese) (See Japanese page) 
(in English) surprising / business description sentences / annual securities report / T5 / text mining / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 173, NLC2024-2, pp. 6-11, Sept. 2024.
Paper # NLC2024-2 
Date of Issue 2024-08-26 (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 NLC2024-2

Conference Information
Committee NLC  
Conference Date 2024-09-02 - 2024-09-03 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido University. The Clark Memorial Student Center. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) The 21st Text Analytics Symposium 
Paper Information
Registration To NLC 
Conference Code 2024-09-NLC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Extraction of sentences containing surprising business in companies 
Sub Title (in English)  
Keyword(1) surprising  
Keyword(2) business description sentences  
Keyword(3) annual securities report  
Keyword(4) T5  
Keyword(5) text mining  
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1st Author's Name Yoshimasa Kobayashi  
1st Author's Affiliation SEIKEI University (SEIKEI Univ.)
2nd Author's Name Hiroyuki Sakai  
2nd Author's Affiliation SEIKEI University (SEIKEI Univ.)
3rd Author's Name Kengo Enami  
3rd Author's Affiliation Tsuda University (Tsuda Univ.)
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Speaker Author-1 
Date Time 2024-09-02 13:30:00 
Presentation Time 20 minutes 
Registration for NLC 
Paper # NLC2024-2 
Volume (vol) vol.124 
Number (no) no.173 
Page pp.6-11 
#Pages 6 
Date of Issue 2024-08-26 (NLC) 

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