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Paper Abstract and Keywords
Presentation 2017-06-25 09:55
Stochastic Divergence Minimization for Biterm Topic Model
Zhenghang Cui (Univ. of Tokyo), Issei Sato (Univ. of Tokyo/RIKEN), Masashi Sugiyama (RIKEN/Univ. of Tokyo) IBISML2017-7
Abstract (in Japanese) (See Japanese page) 
(in English) Inferring latent topics of collected short texts is useful for understanding its hidden structure and predicting new contents. A biterm topic model (BTM) was recently proposed for analyzing short texts.Stochastic inference algorithms based on collapsed Gibbs sampling (CGS) and collapsed variational inference have been proposed for BTM. However, they either require large computational complexity, or rely on very crude estimation. In this paper, we develop a stochastic divergence minimization inference algorithm for BTM to estimate latent topics more accurately in a scalable way. Experiments demonstrate the superiority of our proposed algorithm compared with existing inference algorithms.
Keyword (in Japanese) (See Japanese page) 
(in English) short text / topic model / biterm / stochastic inference algorithm / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 110, IBISML2017-7, pp. 185-192, June 2017.
Paper # IBISML2017-7 
Date of Issue 2017-06-16 (IBISML) 
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)
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Conference Information
Committee NC IPSJ-BIO IBISML IPSJ-MPS  
Conference Date 2017-06-23 - 2017-06-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Institute of Science and Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine Learning Approach to Biodata Mining, and General 
Paper Information
Registration To IBISML 
Conference Code 2017-06-NC-BIO-IBISML-MPS 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Stochastic Divergence Minimization for Biterm Topic Model 
Sub Title (in English)  
Keyword(1) short text  
Keyword(2) topic model  
Keyword(3) biterm  
Keyword(4) stochastic inference algorithm  
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1st Author's Name Zhenghang Cui  
1st Author's Affiliation The University of Tokyo (Univ. of Tokyo)
2nd Author's Name Issei Sato  
2nd Author's Affiliation The University of Tokyo/RIKEN (Univ. of Tokyo/RIKEN)
3rd Author's Name Masashi Sugiyama  
3rd Author's Affiliation RIKEN/The University of Tokyo (RIKEN/Univ. of Tokyo)
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Speaker Author-1 
Date Time 2017-06-25 09:55:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # IBISML2017-7 
Volume (vol) vol.117 
Number (no) no.110 
Page pp.185-192 
#Pages 8 
Date of Issue 2017-06-16 (IBISML) 

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