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Authors: Nguyen Minh The ; Takahiro Kawamura ; Hiroyuki Nakagawa ; Yasuyuki Tahara and Akihiko Ohsuga

Affiliation: The University of Electro-Communications, Japan

ISBN: 978-989-8425-22-5

Keyword(s): Human activity, Semantic network, Web mining, Self-supervised learning, Conditional random fields.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Business Analytics ; Cloud Computing ; Computational Intelligence ; Data and Information Retrieval ; Data Engineering ; Data Semantics ; Enterprise Information Systems ; Evolutionary Computing ; Information Systems Analysis and Specification ; Knowledge Acquisition ; Knowledge Discovery and Information Retrieval ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Machine Learning ; Natural Language Processing ; Ontologies and the Semantic Web ; Ontology Engineering ; Pattern Recognition ; Semantic Web Technologies ; Services Science ; Soft Computing ; Software Agents and Internet Computing ; Symbolic Systems

Abstract: The goal of this paper is to describe a method to automatically extract all basic attributes namely actor, action, object, time and location which belong to an activity, and the relationships (transition and cause) between activities in each sentence retrieved from Japanese CGM (consumer generated media). Previous work had some limitations, such as high setup cost, inability of extracting all attributes, limitation on the types of sentences that can be handled, insufficient consideration of interdependency among attributes, and inability of extracting causes between activities. To resolve these problems, this paper proposes a novel approach that treats the activity extraction as a sequence labeling problem, and automatically makes its own training data. This approach has advantages such as domain-independence, scalability, and unnecessary hand-tagged data. Since it is unnecessary to fix the positions and the number of the attributes in activity sentences, this approach can extract all attributes and relationships between activities by making only a single pass over its corpus. Additionally, by converting to simpler sentences, removing stop words, utilizing html tags, google map api, and wikipedia, the proposed approach can deal with complex sentences retrieved from Japanese CGM. (More)

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Paper citation in several formats:
Minh The N.; Kawamura T.; Nakagawa H.; Tahara Y.; Ohsuga A. and (2010). AUTOMATIC MINING OF HUMAN ACTIVITY AND ITS RELATIONSHIPS FROM CGM.In Proceedings of the 5th International Conference on Software and Data Technologies - Volume 1: ICSOFT, ISBN 978-989-8425-22-5, pages 285-292. DOI: 10.5220/0002922802850292

@conference{icsoft10,
author={Nguyen {Minh The} and Takahiro Kawamura and Hiroyuki Nakagawa and Yasuyuki Tahara and Akihiko Ohsuga},
title={AUTOMATIC MINING OF HUMAN ACTIVITY AND ITS RELATIONSHIPS FROM CGM},
booktitle={Proceedings of the 5th International Conference on Software and Data Technologies - Volume 1: ICSOFT,},
year={2010},
pages={285-292},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002922802850292},
isbn={978-989-8425-22-5},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Software and Data Technologies - Volume 1: ICSOFT,
TI - AUTOMATIC MINING OF HUMAN ACTIVITY AND ITS RELATIONSHIPS FROM CGM
SN - 978-989-8425-22-5
AU - Minh The, N.
AU - Kawamura, T.
AU - Nakagawa, H.
AU - Tahara, Y.
AU - Ohsuga, A.
PY - 2010
SP - 285
EP - 292
DO - 10.5220/0002922802850292

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