IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2020-12-11 14:00
How to collect teacher data for machine learning models to classify internal document know-how
Takahiro Shimura, Kohei Yabuki, Takumi Hasegawa (Kyosan Electric Mfg), Shiva Krishna Maheshuni, Takeshi Mizuma (Univ.Tokyo) DC2020-62
Abstract (in Japanese) (See Japanese page) 
(in English) Not many companies seem to be able to utilize the product design know-how in their internal documents across the board.
The purpose of this study is to establish a method of constructing a machine learning model to classify design know-how from internal documents and to prepare the groundwork for application implementation to support cross-sectional utilization of know-how.
In this paper, we describe the philosophy and implementation method of the tool we have implemented to collect teacher data for the machine learning model, and propose a formula for calculating the usefulness of know-how that allows to easily compare the quality of know-how.
Keyword (in Japanese) (See Japanese page) 
(in English) Know-how / Knowledge Management / Machine Learning / Teacher Data / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 288, DC2020-62, pp. 18-22, Dec. 2020.
Paper # DC2020-62 
Date of Issue 2020-12-04 (DC) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 DC2020-62

Conference Information
Committee DC  
Conference Date 2020-12-11 - 2020-12-11 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To DC 
Conference Code 2020-12-DC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) How to collect teacher data for machine learning models to classify internal document know-how 
Sub Title (in English)  
Keyword(1) Know-how  
Keyword(2) Knowledge Management  
Keyword(3) Machine Learning  
Keyword(4) Teacher Data  
1st Author's Name Takahiro Shimura  
1st Author's Affiliation Kyosan Electric Manufacturing Co., Ltd. (Kyosan Electric Mfg)
2nd Author's Name Kohei Yabuki  
2nd Author's Affiliation Kyosan Electric Manufacturing Co., Ltd. (Kyosan Electric Mfg)
3rd Author's Name Takumi Hasegawa  
3rd Author's Affiliation Kyosan Electric Manufacturing Co., Ltd. (Kyosan Electric Mfg)
4th Author's Name Shiva Krishna Maheshuni  
4th Author's Affiliation The University of Tokyo (Univ.Tokyo)
5th Author's Name Takeshi Mizuma  
5th Author's Affiliation The University of Tokyo (Univ.Tokyo)
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Speaker Author-1 
Date Time 2020-12-11 14:00:00 
Presentation Time 20 minutes 
Registration for DC 
Paper # DC2020-62 
Volume (vol) vol.120 
Number (no) no.288 
Page pp.18-22 
Date of Issue 2020-12-04 (DC) 

[Return to Top Page]

[Return to IEICE Web Page]

The Institute of Electronics, Information and Communication Engineers (IEICE), Japan