Text categorization based on combination of modified back propagation neural network and latent semantic analysis |
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Authors: | Wei Wang Bo Yu |
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Affiliation: | (1) Institute of Image and Information, School of Electronics and Information, Sichuan University, Chengdu, 610065, China;(2) School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China |
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Abstract: | This paper proposed a new text categorization model based on the combination of modified back propagation neural network (MBPNN)
and latent semantic analysis (LSA). The traditional back propagation neural network (BPNN) has slow training speed and is
easy to trap into a local minimum, and it will lead to a poor performance and efficiency. In this paper, we propose the MBPNN
to accelerate the training speed of BPNN and improve the categorization accuracy. LSA can overcome the problems caused by
using statistically derived conceptual indices instead of individual words. It constructs a conceptual vector space in which
each term or document is represented as a vector in the space. It not only greatly reduces the dimension but also discovers
the important associative relationship between terms. We test our categorization model on 20-newsgroup corpus and reuter-21578
corpus, experimental results show that the MBPNN is much faster than the traditional BPNN. It also enhances the performance
of the traditional BPNN. And the application of LSA for our system can lead to dramatic dimensionality reduction while achieving
good classification results. |
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