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Paper Abstract and Keywords
Presentation 2018-06-01 15:55
Readability Categorization of Japan EIKEN Document using Machine Learning with TF-IDF
Rupasingha Arachchilage Hiruni Madhusha Rupasingha, Takeda Yui, Incheon Paik (UoA) SC2018-7
Abstract (in Japanese) (See Japanese page) 
(in English) Understanding of the readability level and improvements of the text are needed for a specific audience. Accordingly, automatic measurement of text readability has been important issue and there have been many approaches to solve it. Classical readability is measured by readability formulas, and more recent research have employed machine learning algorithms. However, those machine learning approaches use complex features such as linguistic and grammatical features to hinder calculating correct readability score. In this paper, we investigate which features available as input to machine learning improves the performance more easily and accurately. We experiment the readability categorization by three kinds of feature vectors: readability score, Term Frequency-Inverse Document Frequency (TF-IDF), and the combination of them. The classification results by TF-IDF only give accurate results than other two features.
Keyword (in Japanese) (See Japanese page) 
(in English) Readability / Eiken Document / Document Classification / Machine Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 72, SC2018-7, pp. 37-42, June 2018.
Paper # SC2018-7 
Date of Issue 2018-05-25 (SC) 
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)
Download PDF SC2018-7

Conference Information
Committee SC  
Conference Date 2018-06-01 - 2018-06-02 
Place (in Japanese) (See Japanese page) 
Place (in English) UBIC 3D Theater, University of Aizu 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Service Computing for the 4th Industrial Revolution and Other Issues 
Paper Information
Registration To SC 
Conference Code 2018-06-SC 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Readability Categorization of Japan EIKEN Document using Machine Learning with TF-IDF 
Sub Title (in English)  
Keyword(1) Readability  
Keyword(2) Eiken Document  
Keyword(3) Document Classification  
Keyword(4) Machine Learning  
1st Author's Name Rupasingha Arachchilage Hiruni Madhusha Rupasingha  
1st Author's Affiliation University of Aizu (UoA)
2nd Author's Name Takeda Yui  
2nd Author's Affiliation University of Aizu (UoA)
3rd Author's Name Incheon Paik  
3rd Author's Affiliation University of Aizu (UoA)
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Speaker Author-1 
Date Time 2018-06-01 15:55:00 
Presentation Time 25 minutes 
Registration for SC 
Paper # SC2018-7 
Volume (vol) vol.118 
Number (no) no.72 
Page pp.37-42 
Date of Issue 2018-05-25 (SC) 

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