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Paper Abstract and Keywords
Presentation 2018-02-17 10:30
A Method of Extracting Related Terms in a Specialty Area
Satoshi Sunaga, Tsunenari Saitoh, Hiroshi Miyao, Yamato Harada (NTT) NLC2017-50
Abstract (in Japanese) (See Japanese page) 
(in English) In a variety of information retrieval, if a related term dictionary exists,
it is effective because it can be used for associative retrieval and fuzzy search.
However, it is costly to manually construct and update a related term dictionary.
Therefore, we are working on automatically extracting relevant words using
co-occurrence of words from document files.
In related word extraction by co-occurrence, there are two problems.
These problems are that unrelated or irrelevant words (incorrect related words)
are extracted and there are unextracted correct related words.
For the former problem, we are working on finding the features of incorrect
related words and solve them by excluding them.
However, the latter problem requires a strategy to increase the number of
cooccurrent words to be extracted so as to include correct related words
before excluding incorrect related terms.
In this paper, as a method to increase the number of co-occurring words,
we propose a method for extracting related word candidates by co-occurrence
from synonyms.
The effectiveness of the proposed method by experiment is shown.
In addition, as a consideration, we explain that similar relation appears
between the range of correct related words extracted and the range meaning
of synonyms.
Keyword (in Japanese) (See Japanese page) 
(in English) Full-text Search / Information Extraction / Related Terms / Co-occurrence / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 439, NLC2017-50, pp. 51-56, Feb. 2018.
Paper # NLC2017-50 
Date of Issue 2018-02-09 (NLC) 
ISSN Print edition: ISSN 0913-5685    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 NLC2017-50

Conference Information
Committee NLC IPSJ-IFAT  
Conference Date 2018-02-16 - 2018-02-17 
Place (in Japanese) (See Japanese page) 
Place (in English) T.O.G. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) The Twelveth Text Analytics Symposium 
Paper Information
Registration To NLC 
Conference Code 2018-02-NLC-IFAT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Method of Extracting Related Terms in a Specialty Area 
Sub Title (in English)  
Keyword(1) Full-text Search  
Keyword(2) Information Extraction  
Keyword(3) Related Terms  
Keyword(4) Co-occurrence  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Satoshi Sunaga  
1st Author's Affiliation NTT Corporation (NTT)
2nd Author's Name Tsunenari Saitoh  
2nd Author's Affiliation NTT Corporation (NTT)
3rd Author's Name Hiroshi Miyao  
3rd Author's Affiliation NTT Corporation (NTT)
4th Author's Name Yamato Harada  
4th Author's Affiliation NTT Corporation (NTT)
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Speaker Author-1 
Date Time 2018-02-17 10:30:00 
Presentation Time 25 minutes 
Registration for NLC 
Paper # NLC2017-50 
Volume (vol) vol.117 
Number (no) no.439 
Page pp.51-56 
#Pages 6 
Date of Issue 2018-02-09 (NLC) 

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