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Paper Abstract and Keywords
Presentation 2017-12-23 11:50
Detection of mergeable Wikipedia articles based on multiple embedding results
Renzhi Wang, Mizuho Iwaihara (Waseda U.) DE2017-35
Abstract (in Japanese) (See Japanese page) 
(in English) Wikipedia is the largest online encyclopedia, in which articles are edited by different volunteers with different thoughts and styles. Sometimes two or more articles’ titles are different but the themes of these articles are exactly the same or strongly similar. Administrators and editors are supposed to detect these article pairs and determine whether they should be merged together. In this paper, we propose a method to automatically determine whether an article pair should be merged together. We consider both duplicate case and overlap case. In the duplicate case, the articles pairs are covering exactly the same contents. In the overlap case, the articles pairs are covering related subjects that have a significant overlap. The content of an overlap part is similar but the words in the pair are probably different, so methods that exploit semantic relatedness are necessary. To deal with this problem we propose combination of multiple embedding results and rebuild word vectors for detecting mergeable article pairs. We also deal with various mergeable cases by combining distinct text fragments together. Our experiments show that our method performs better than existing embedding methods.
Keyword (in Japanese) (See Japanese page) 
(in English) word embedding / mergeable article / Wikipedia / text mining / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 374, DE2017-35, pp. 79-83, Dec. 2017.
Paper # DE2017-35 
Date of Issue 2017-12-15 (DE) 
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 DE2017-35

Conference Information
Committee DE CEA IPSJ-DBS  
Conference Date 2017-12-22 - 2017-12-23 
Place (in Japanese) (See Japanese page) 
Place (in English) National Institute of Informatics 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To DE 
Conference Code 2017-12-DE-CEA-DBS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Detection of mergeable Wikipedia articles based on multiple embedding results 
Sub Title (in English)  
Keyword(1) word embedding  
Keyword(2) mergeable article  
Keyword(3) Wikipedia  
Keyword(4) text mining  
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1st Author's Name Renzhi Wang  
1st Author's Affiliation Waseda University (Waseda U.)
2nd Author's Name Mizuho Iwaihara  
2nd Author's Affiliation Waseda University (Waseda U.)
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Speaker Author-1 
Date Time 2017-12-23 11:50:00 
Presentation Time 25 minutes 
Registration for DE 
Paper # DE2017-35 
Volume (vol) vol.117 
Number (no) no.374 
Page pp.79-83 
#Pages 5 
Date of Issue 2017-12-15 (DE) 

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