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Paper Abstract and Keywords
Presentation 2020-09-10 10:05
Extraction of medical technology sentences from the Faculty of Medicine Websites
Yuta Sugawara, Kaito Takano, Hiroyuki Sakai (Seikei) NLC2020-2
Abstract (in Japanese) (See Japanese page) 
(in English) In this research, we propose a method for extracting sentences including medical technology form the faculty of medicine websites by deep learning. For example, our method extracts sentences related to medical technology being researched in the faculty of medicine such as “As a result of the group's 3-year study and analysis, it was found that the incidence of secondary gastric cancer in the sterilized group was reduced to about 1/3 compared to the non-sterilized group.”. From these sentences, it is possible to know what kind of medical technology is being researched and used in treatment at which the faculty of medicine. Our method automatically generates the training data necessary to extract the articles including medical technology. As a result, medical technology sentences could be extracted with an accuracy of 78% by our method.
Keyword (in Japanese) (See Japanese page) 
(in English) medical technology / automatic training data generation / cue phrases / faculty of medicine websites / imfomation extraction / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 166, NLC2020-2, pp. 7-10, Sept. 2020.
Paper # NLC2020-2 
Date of Issue 2020-09-03 (NLC) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee NLC  
Conference Date 2020-09-10 - 2020-09-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) The Sixteenth Text Analytics Symposium 
Paper Information
Registration To NLC 
Conference Code 2020-09-NLC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Extraction of medical technology sentences from the Faculty of Medicine Websites 
Sub Title (in English)  
Keyword(1) medical technology  
Keyword(2) automatic training data generation  
Keyword(3) cue phrases  
Keyword(4) faculty of medicine websites  
Keyword(5) imfomation extraction  
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1st Author's Name Yuta Sugawara  
1st Author's Affiliation Seikei University (Seikei)
2nd Author's Name Kaito Takano  
2nd Author's Affiliation Seikei University (Seikei)
3rd Author's Name Hiroyuki Sakai  
3rd Author's Affiliation Seikei University (Seikei)
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Speaker Author-1 
Date Time 2020-09-10 10:05:00 
Presentation Time 25 minutes 
Registration for NLC 
Paper # NLC2020-2 
Volume (vol) vol.120 
Number (no) no.166 
Page pp.7-10 
#Pages
Date of Issue 2020-09-03 (NLC) 


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