Adaptive feature spectrum neural networks for special types of natural language classification

Abstract

The improvement of computer computing power has led to the rapid development of deep learning algorithms.However,due to the special word order,wording,structure,sentence structure,grammatical structure,and expression of ancient poetry,deep learning models need to consume more computing power for feature extraction,etc.Therefore,it has not been widely used in this field.As a result,a new kind neural network:the adaptive feature spectrum neural network was proposed,which can considerably reduce the computation and adaptively select the features that are the most useful for classification in order to form the most efficient feature spectrum.The classification results obtained have certain interpretability.Moreover,its fast running speed and lower RAM consumption make it very suitable for learning aids software,and other fields.Based on this algorithm,a corresponding personalized learning platform was developed.This algorithm improves the classification accuracy of ancient Chinese poetry from 93.84% to 99%.

Keywords

• adaptive feature spectrum;neural network;text classification;ancient poems;laplace matrix