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Authors: Nuo Zhang ; Daisuke Matsuzaki ; Toshinori Watanabe and Hisashi Koga

Affiliation: Graduate School of Information Systems, The University of Electro-Communications, Japan

ISBN: 978-989-8111-66-1

Keyword(s): Document analysis, PRDC, Topic extraction, Relation analysis, Clustering, Data compression.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Data Manipulation ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Representation and Reasoning ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

Abstract: Nowadays, there are a great deal of e-documents can be easily accessed. It will be beneficial if a method can evaluate documents and abstract significant content. Similarity analysis and topic extraction are widely used as document relation analysis techniques. Most of the methods are based on dictionary-base morphological analysis. They cannot meet the requirement when the Internet grows fast and new terms appear but dictionary cannot be automatically updated fast enough. In this study, we propose a novel document relation analysis (topic extraction) method based on a compressibility vector. Our proposal does not require morphological analysis, and it can automatically evaluate input documents. We will examine the proposal with using model document and Reuters-21578 dataset, for relation analysis and topic extraction. The effectiveness of the proposed method will be shown in simulations.

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Paper citation in several formats:
Zhang N.; Matsuzaki D.; Watanabe T.; Koga H. and (2009). DOCUMENT RELATION ANALYSIS BASED ON COMPRESSIBILITY VECTOR.In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8111-66-1, pages 255-260. DOI: 10.5220/0001660202550260

@conference{icaart09,
author={Nuo Zhang and Daisuke Matsuzaki and Toshinori Watanabe and Hisashi Koga},
title={DOCUMENT RELATION ANALYSIS BASED ON COMPRESSIBILITY VECTOR},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2009},
pages={255-260},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001660202550260},
isbn={978-989-8111-66-1},
}

TY - CONF

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - DOCUMENT RELATION ANALYSIS BASED ON COMPRESSIBILITY VECTOR
SN - 978-989-8111-66-1
AU - Zhang, N.
AU - Matsuzaki, D.
AU - Watanabe, T.
AU - Koga, H.
PY - 2009
SP - 255
EP - 260
DO - 10.5220/0001660202550260

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