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Document classification system based on HMM word map

Authors:

Tambouratzis GeorgeAuthors Info & Claims

CSTST '08: Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology

Pages 7 - 12

https://doi.org/10.1145/1456223.1456229

Published: 28 October 2008 Publication History

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Abstract

In this article, a system based on Hidden Markov Models (HMM) for document organization is presented. The purpose of the system is the classification of a document collection in terms of document content. The system possesses a two-level hybrid connectionist architecture that comprises (i) an automatically created word map using a HMM, which functions as a feature extraction module and (ii) a supervised MLP-based classifier, which provides the final classification result. A series of experiments, which have been performed on Modern Greek text-only documents, is presented. These experiments illustrate the effectiveness of the proposed system.

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Tsimboukakis N. and Tambouratzis G., "Self-Organizing Word Map for Context-Based Document Classification", In Proceedings of WSOM'07, 6^th International Workshop on Self-Organizing Maps, 2007

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Cited By

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Celard PIglesias ESorribes-Fdez JRomero RVieira ABorrajo L(2022)Improving Short Query Representation in LDA Based Information Retrieval SystemsHybrid Artificial Intelligent Systems10.1007/978-3-031-15471-3_10(111-122)Online publication date: 12-Sep-2022
https://doi.org/10.1007/978-3-031-15471-3_10
Celard PVieira AIglesias EBorrajo L(2020)LDA filter: A Latent Dirichlet Allocation preprocess method for WekaPLOS ONE10.1371/journal.pone.024170115:11(e0241701)Online publication date: 9-Nov-2020
https://doi.org/10.1371/journal.pone.0241701
Borrajo LVieira AIglesias E(2020)An HMM-based synthetic view generator to improve the efficiency of ensemble systemsLogic Journal of the IGPL10.1093/jigpal/jzz067Online publication date: 13-Jan-2020
https://doi.org/10.1093/jigpal/jzz067
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Index Terms

Document classification system based on HMM word map
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection
    2. Search engine architectures and scalability
      1. Search engine indexing

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Published In

CSTST '08: Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology

October 2008

733 pages

ISBN:9781605580463

DOI:10.1145/1456223

Conference Chair:
Lotfi A. Zadeh
University of California
,
General Chairs:
Youakim Badr
INSA-Lyon, France
,
Ajith Abraham
Norwegian University of Science and Technology, Norway
,
Yukio Ohsawa
Tokyo Institute of Technology, Japan
,
Program Chairs:
Richard Chbeir
University of Bourgogne, France
,
Fernando Ferri
IRPPS-CNR, Italy
,
Mario Koeppen
Kyushu Institute of Technology, Japan
,
Dominique Laurent
University of Cergy-Pontoise, France

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 October 2008

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Cited By

View all

Celard PIglesias ESorribes-Fdez JRomero RVieira ABorrajo L(2022)Improving Short Query Representation in LDA Based Information Retrieval SystemsHybrid Artificial Intelligent Systems10.1007/978-3-031-15471-3_10(111-122)Online publication date: 12-Sep-2022
https://doi.org/10.1007/978-3-031-15471-3_10
Celard PVieira AIglesias EBorrajo L(2020)LDA filter: A Latent Dirichlet Allocation preprocess method for WekaPLOS ONE10.1371/journal.pone.024170115:11(e0241701)Online publication date: 9-Nov-2020
https://doi.org/10.1371/journal.pone.0241701
Borrajo LVieira AIglesias E(2020)An HMM-based synthetic view generator to improve the efficiency of ensemble systemsLogic Journal of the IGPL10.1093/jigpal/jzz067Online publication date: 13-Jan-2020
https://doi.org/10.1093/jigpal/jzz067
Momenzadeh MSehhati MRabbani H(2020)Using hidden Markov model to predict recurrence of breast cancer based on sequential patterns in gene expression profilesJournal of Biomedical Informatics10.1016/j.jbi.2020.103570111(103570)Online publication date: Nov-2020
https://doi.org/10.1016/j.jbi.2020.103570
Iglesias EVieira ADiz L(2016)An HMM-Based Multi-view Co-training Framework for Single-View Text CorporaHybrid Artificial Intelligent Systems10.1007/978-3-319-32034-2_6(66-78)Online publication date: 14-Apr-2016
https://doi.org/10.1007/978-3-319-32034-2_6
Borrajo LSeara Vieira AIglesias E(2015)TCBR-HMMApplied Soft Computing10.1016/j.asoc.2014.10.01926:C(463-473)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1016/j.asoc.2014.10.019
Vieira AIglesias EBorrajo L(2014)T-HMM: A Novel Biomedical Text Classifier Based on Hidden Markov Models8th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014)10.1007/978-3-319-07581-5_27(225-234)Online publication date: 2014
https://doi.org/10.1007/978-3-319-07581-5_27

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