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Paper Abstract and Keywords
Presentation 2019-11-28 15:15
Effectiveness of feature selection as pre-processing of LSTM using W2V
Shiori Koga, Tsunenori Mine, Sachio Hirokawa (Kyushu Univ.) AI2019-34
Abstract (in Japanese) (See Japanese page) 
(in English) Among RNNs, especially LSTM is capable of long-term memory, and can be expected to acquire information including better contextual information, and has been used for various problems including classification problems. In such classification problems, various attempts have been made to improve the accuracy of classification by selecting features such as words that contribute to classification from among the words that make up sentences.However, since LSTM attaches importance to the context, no attempt has been made to select words in LSTM input as far as we know.In this study, we show the effectiveness of using Word2Vec as a word expression in an LSTM input sentence and performing feature selection as a pre-processing of the LSTM input. The results show that the accuracy of classification improves when feature selection is performed.
Keyword (in Japanese) (See Japanese page) 
(in English) text classification / LSTM / feature selection / Word2Vec / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 317, AI2019-34, pp. 25-30, Nov. 2019.
Paper # AI2019-34 
Date of Issue 2019-11-21 (AI) 
ISSN 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 AI2019-34

Conference Information
Committee AI  
Conference Date 2019-11-28 - 2019-11-28 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To AI 
Conference Code 2019-11-AI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Effectiveness of feature selection as pre-processing of LSTM using W2V 
Sub Title (in English)  
Keyword(1) text classification  
Keyword(2) LSTM  
Keyword(3) feature selection  
Keyword(4) Word2Vec  
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1st Author's Name Shiori Koga  
1st Author's Affiliation Kyushu University (Kyushu Univ.)
2nd Author's Name Tsunenori Mine  
2nd Author's Affiliation Kyushu University (Kyushu Univ.)
3rd Author's Name Sachio Hirokawa  
3rd Author's Affiliation Kyushu University (Kyushu Univ.)
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Speaker Author-1 
Date Time 2019-11-28 15:15:00 
Presentation Time 25 minutes 
Registration for AI 
Paper # AI2019-34 
Volume (vol) vol.119 
Number (no) no.317 
Page pp.25-30 
#Pages 6 
Date of Issue 2019-11-21 (AI) 

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