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ThinkMind // CONTENT 2010, The Second International Conference on Creative Content Technologies // View article content_2010_1_10_60029


Classifying Content Mode of Organizational Texts Using Simple Neural and Neuro-Fuzzy Approaches

Authors:
Maryam Tayefeh Mahmoudi
Babak Nadjar Araabi
Kambiz Badie
Nafiseh Forouzideh

Keywords: text classification; neural network; neuro-fuzzy approach; organizational task; content mode.

Abstract:
In this paper, we present simple neural and neuro-fuzzy approaches to classify the mode of a text’s content which is organized for helping users with their organizational tasks. In this regard, 7 major features were chosen as inputs for our suggested approaches. 3 nominal values L, M, and H were used as the possible values for each feature. Results of experimentation on a dataset including 540 data show the fact that the Takagi-Sugeno as a neuro-fuzzy approach using lolimot learning algorithm, performs better compared to multi-layer perceptron and radial basis function as simple neural approaches. Due to the high performance of this approach, it is expected to be successfully applicable to a wide range of content mode classification issues in decision support environment.

Pages: 1 to 6

Copyright: Copyright (c) IARIA, 2010

Publication date: November 21, 2010

Published in: conference

ISSN: 2308-4162

ISBN: 978-1-61208-110-6

Location: Lisbon, Portugal

Dates: from November 21, 2010 to November 26, 2010

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