Tang Chang'an poetry automatic classification: a practical application of deep learning methods

Abstract

As the capital of Tang Dynasty, Chang'an was one of the most prosperous cities in the world at that time and had a profound influence on Tang poetry. Poets described Chang'an to illustrate the cultural features of the Tang Dynasty while also invoking emotions in readers. The study of Tang Chang'an poetry has important literary and historical value. In order to understand the interpretation and emotional expression of Tang Chang'an poetry more conveniently and clearly, we conducted a study using deep learning to classify Chang'an poetry into four classes: imperially assigned poetry (greek language), emotional poetry (greek language), parting poetry (greek language), and other poetry (greek language). We suggested a comprehensive framework of text classification based on deep learning, including a text input module, feature encoder module, and classification module. We applied several mainstream deep neural network structures to extract features in different ways, which comprised convolutional neural network (CNN), Fasttext, bi-direction long-short-term memory network, and Attention mechanism. Based on our experimental findings, the CNN-based method achieved the best performance for the task. Our inference was that, in Chinese ancient poetry, the analysis of semantic content is more facilitated by local textual features rather than contextual features. We combined this inference with the theory of image in Chinese ancient poetry to analyze the suitability of the deep learning techniques for the study of Chinese ancient poetry.