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 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) |
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NLC2020-2 |
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) |
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medical technology |
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automatic training data generation |
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cue phrases |
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faculty of medicine websites |
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imfomation extraction |
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1st Author's Name |
Yuta Sugawara |
1st Author's Affiliation |
Seikei University (Seikei) |
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Kaito Takano |
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Seikei University (Seikei) |
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Hiroyuki Sakai |
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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 |
4 |
Date of Issue |
2020-09-03 (NLC) |
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