A novel text representation model to categorize text documents using convolution neural network

Автор: M. B. Revanasiddappa, B. S. Harish

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 5 vol.11, 2019 года.

Бесплатный доступ

This paper presents a novel text representation model called Convolution Term Model (CTM) for effective text categorization. In the process of text categorization, representation plays a very primary role. The proposed CTM is based on Convolution Neural Network (CNN). The main advantage of proposed text representation model is that, it preserves semantic relationship and minimizes the feature extraction burden. In proposed model, initially convolution filter is applied on word embedding matrix. Since, the resultant CTM matrix is higher dimension, feature selection methods are applied to reduce the CTM feature space. Further, selected CTM features are fed into classifier to categorize text document. To discover the effectiveness of the proposed model, extensive experimentations are carried out on four standard benchmark datasets viz., 20-NewsGroups, Reuter-21758, Vehicle Wikipedia and 4 University datasets using five different classifiers. Accuracy is used to assess the performance of classifiers. The proposed model shows impressive results with all classifiers.

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Text Documents, Convolution Neural Network, Representation, Feature Selection, Categorization

Короткий адрес: https://readera.ru/15016594

IDR: 15016594   |   DOI: 10.5815/ijisa.2019.05.05

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