NDLI: Artificial Neural Network for Document Classification Using Latent Semantic Indexing

Content Provider	IEEE Xplore Digital Library
Author	Cheng Hua Li Soon Cheol Park
Copyright Year	2007
Description	Author affiliation: Chonbuk Nat. Univ., Jeonju (Cheng Hua Li; Soon Cheol Park)
Abstract	In this study, we construct document classification systems using artificial neural network training by the multi-output perceptron learning algorithm (MOPL) and back-propagation neural network (BPNN). Most classic classification systems represent the contents of documents with a set of index terms, which is termed the vector space model (VSM). However, this method requires a high dimensional space to represent the documents, and it does not take into account the semantic relationship between the terms, which could lead to a poor classification performance. In this paper, we introduce latent semantic indexing (LSI) in our systems. It could not only reduce the dimensionality to a great extent but also determine important associative relationships between the terms. The LSI also aids in accelerating the training speed and improves the classification accuracy. We test our classification systems on the standard Reuter-21578 collection. The experimental evaluations show that the system training with the LSI is considerably faster than the original system training with the VSM and that the former yields better classification results.
Starting Page	17
Ending Page	21
File Size	136581
Page Count	5
File Format	PDF
ISBN	0769530451
DOI	10.1109/ISITC.2007.69
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-23
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning algorithms Neural networks Artificial neural networks Information retrieval Large scale integration Inference algorithms Internet Labeling Information technology Indexing
Content Type	Text
Resource Type	Article

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