Publication - Word map systems for content-based document classification

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Word map systems for content-based document classification

Research Area:  
Other topics in Computer Science
    
Type:  
Journal article

 

Year: 2011
Authors: N. Tsimboukakis; George Tambouratzis
Journal: IEEE Transactions on Systems, Man & Cybernetics – Part C
Pages: in print
DOI: 10.1109/TSMCC.2010.2096416
Abstract:
The main purpose of this paper is the classification of documents in terms of their content. Two systems are presented here that share a two-level architecture that include 1) a word map created via unsupervised learning that functions as a document-representation module and 2) a supervised multilayer-perceptron-based classifier. Two approaches to create word maps are presented and compared; these are based on hidden Markov models (HMMs) and the self-organizing map. A series of experiments is performed on several datasets of text-only documents, which are written in either Greek or in English. A comparison with established methods, such as the support-vector machine (SVM), illustrates the effectiveness of the proposed systems.
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