Feature Extraction Using Neural Networks for Vietnamese Text Classification

In this paper, a feature extraction method based on neural networks for Vietnamese text classification is developed. The Vietnamese online news documents are initially preprocessed to transform to lower-case documents as well as remove punctuation and stop-words. Then, the tokenization applying the combination of uni-gram and bi-gram models is conducted to generate a list of tokens for each document. As a result, polyphonic linguistic problems in Vietnamese language are diminished. The statistical term frequency - inverse document frequency model is employed to represent the lists of tokens as real value vectors. Instead of applying conventional feature selection algorithms, neural networks are conducted for dimensionality reduction. Therefore, not only the size of the term frequency - inverse document frequency vectors are reduced, but also distinctive feature vectors are created for text classification tasks. Support vector machines are utilized in the classification step. The results attained in the experiment have shown that our work using neural networks for the feature extraction outperforms other traditional methods.