Text categorization algorithms using semantic approaches, corpus-based thesaurus and WordNet

In this paper, a corpus-based thesaurus and WordNet were used to improve text categorization performance. We employed the k-NN algorithm and the back propagation neural network (BPNN) algorithms as the classifiers. The k-NN is a simple and famous approach for categorization, and the BPNNs has been widely used in the categorization and pattern recognition fields. However the standard BPNN has some generally acknowledged limitations, such as a slow training speed and can be easily trapped into a local minimum. To alleviate the problems of the standard BPNN, two modified versions, Morbidity neurons Rectified BPNN (MRBP) and Learning Phase Evaluation BPNN (LPEBP), were considered and applied to the text categorization. We conducted the experiments on both the standard reuter-21578 data set and the 20 Newsgroups data set. Experimental results showed that our proposed methods achieved high categorization effectiveness as measured by the precision, recall and F-measure protocols.

► The back propagation neural network (BPNN) algorithms are employed as the classifiers.
► Two modified versions, Morbidity neurons Rectified BPNN and Learning Phase Evaluation BPNN are used to text categorization.
► The modified versions are combined with a corpus-based thesaurus and WordNet.
► We examine them on the standard reuter-21578 data set and the 20 Newsgroups data set.
► They show high effective results measured by the precision, recall and F-measure protocols.