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Paper Abstract and Keywords
Presentation 2018-12-12 11:00
Data augmentation using stereotypical reply for patients' tweet identification
Reine Asakawa, Tomoyosi Akiba (TUT) NLC2018-31
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we try to identify patients' tweets for symptom surveillance using Twitter.
This functionality is indispensable for developing a system Identifying disease epidemic.
Most previous work employed a supervised machine learning methods.
In general, they need a large amount of labeled corpus, which are very expensive to be created.
In order to cope with this problem, we proposed a method to automatically acquire training corpus from Twitter by using a typical response to a patient.
In this paper, we propose a data augmentation approach that extends a training data for RNN-based patient identifier with those automatically acquired corpus.
The method consists of two steps.
As the first step, initial parameters of identifier are trained by the automatically required large corpus.
As the Second step, they are continuously trained by using a small amount of training corpus annotated manually.
By this method, it is possible to effectively combine two kinds of corpus in a manner complementing each other.
We experimented to apply the proposed data augmentation method for the training of RNN-based patient identifiers.
The result showed the proposed model successfully improved the identification performance over the model without data augmentation.
Keyword (in Japanese) (See Japanese page) 
(in English) RNN / Twitter / DataAugmentation / Fine-tuning / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 355, NLC2018-31, pp. 55-60, Dec. 2018.
Paper # NLC2018-31 
Date of Issue 2018-12-04 (NLC) 
ISSN 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)
Notes on Review This article is a technical report without peer review, and its polished version will be published elsewhere.
Download PDF NLC2018-31

Conference Information
Conference Date 2018-12-10 - 2018-12-12 
Place (in Japanese) (See Japanese page) 
Place (in English) Waseda Univ. Nishiwaseda Campus 
Topics (in Japanese) (See Japanese page) 
Topics (in English) The 5th Natural Language Processing Symposium & The 20th Spoken Language Symposium 
Paper Information
Registration To NLC 
Conference Code 2018-12-NLC-NL-SP-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Data augmentation using stereotypical reply for patients' tweet identification 
Sub Title (in English)  
Keyword(1) RNN  
Keyword(2) Twitter  
Keyword(3) DataAugmentation  
Keyword(4) Fine-tuning  
1st Author's Name Reine Asakawa  
1st Author's Affiliation Toyohashi University of Technology (TUT)
2nd Author's Name Tomoyosi Akiba  
2nd Author's Affiliation Toyohashi University of Technology (TUT)
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Speaker Author-1 
Date Time 2018-12-12 11:00:00 
Presentation Time 30 minutes 
Registration for NLC 
Paper # NLC2018-31 
Volume (vol) vol.118 
Number (no) no.355 
Page pp.55-60 
Date of Issue 2018-12-04 (NLC) 

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