Deep learning model for unstructured knowledge classification using structural features

Abstract

Automatic text classification is widely used as the basic method for analyzing data. While classification methods like the support vector machine (SVM) have exhibited impressive performance in the area, the recent use of deep learning has led to considerable progress in text classification. This study proposes a deep learning–based classification model called DEEP-I to classify information on national research and development with complex structural features, a large amount of text, and large-scale classes. In addition to the word–sentence structure of a simple document, the number of stacking layers of the deep model is increased in light of the higher-level structure of the items. Experiments on 180,000 datasets and 366 classification schemes showed that the proposed model can improve classification performance by 22.7% over the traditional SVM and 15.7% over a deep learning model that uses only structured features of word sentences. This improvement was achieved because the multi-layered stacking method was applied to enhance learning by increasing depth by five to 10 times that of the conventional deep learning model and effectively combining features of heterogeneous items. The proposed model is also applicable to datasets containing documents with complex structures.