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Paper Abstract and Keywords
Presentation 2010-01-22 11:30
Self-Supervised Mining Human Activity from the Web
Nguyen Minh The, Takahiro Kawamura, Hiroyuki Nakagawa, Yasuyuki Tahara, Akihiko Ohsuga (The Univ. of Electro-Comm.) AI2009-22
Abstract (in Japanese) (See Japanese page) 
(in English) In our definition, human activity can be expressed by five basic attributes: actor, action, object, time and location. The goal of this paper is describe a novel method to automatically extract all of the basic attributes and the transition between activities derived from sentences in Japanese web pages. However, previous work had some limitations, such as high setup costs, inability to extract all attributes, limitation on the types of sentences that can be handled, and insufficient consideration interdependency among attributes. To resolve these problems, this paper proposes a novel approach that uses conditional random fields and self-supervised learning. This approach treats activity extraction as a sequence labeling problem, and has advantages such as domain-independence, scalability, and does not
require any human input. Since it is unnecessary to fix the number of elements in a tuple, this approach can extract all of the basic attributes and the transition between activities by making only a simgle pass. In an experiment, this approach achieves high precision (activity: 88.9%, attributes: over 90%, transition: over 87%).
Keyword (in Japanese) (See Japanese page) 
(in English) Human Activity / Semantic Network / Web Mining / Self-Supervised Learning / Real-world Agent / / /  
Reference Info. IEICE Tech. Rep., vol. 109, no. 386, AI2009-22, pp. 19-24, Jan. 2010.
Paper # AI2009-22 
Date of Issue 2010-01-15 (AI) 
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 AI2009-22

Conference Information
Committee AI  
Conference Date 2010-01-22 - 2010-01-22 
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 2010-01-AI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Self-Supervised Mining Human Activity from the Web 
Sub Title (in English)  
Keyword(1) Human Activity  
Keyword(2) Semantic Network  
Keyword(3) Web Mining  
Keyword(4) Self-Supervised Learning  
Keyword(5) Real-world Agent  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Nguyen Minh The  
1st Author's Affiliation The University of Electro-Communications (The Univ. of Electro-Comm.)
2nd Author's Name Takahiro Kawamura  
2nd Author's Affiliation The University of Electro-Communications (The Univ. of Electro-Comm.)
3rd Author's Name Hiroyuki Nakagawa  
3rd Author's Affiliation The University of Electro-Communications (The Univ. of Electro-Comm.)
4th Author's Name Yasuyuki Tahara  
4th Author's Affiliation The University of Electro-Communications (The Univ. of Electro-Comm.)
5th Author's Name Akihiko Ohsuga  
5th Author's Affiliation The University of Electro-Communications (The Univ. of Electro-Comm.)
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Speaker Author-1 
Date Time 2010-01-22 11:30:00 
Presentation Time 30 minutes 
Registration for AI 
Paper # AI2009-22 
Volume (vol) vol.109 
Number (no) no.386 
Page pp.19-24 
#Pages 6 
Date of Issue 2010-01-15 (AI) 

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