Tang Chang'an poetry automatic classification: a practical application of deep learning methods.
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 p...
| Publicado en: | Digital Scholarship in the Humanities Vol. 39; no. 2; pp. 756 - 765 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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Oxford University Press / USA
Jun2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=177947259&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 177947259 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Jun2024 vid: 39 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 177947259 10.1093/llc/fqae014 ppf: 756 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.8MB tig: atl: Tang Chang'an poetry automatic classification: a practical application of deep learning methods. aug: au: Tian, Mengmeng Jia, Qi Wang, Cong Yang, Juwang Liu, Xin affil: Teachers' College of Beijing Union University , Beijing, China School of Computer & Communication Engineering, University of Science & Technology Beijing , Beijing, China School of Culture Communication and Art, China Women's University , Beijing, China su: Artificial neural networks Deep learning Convolutional neural networks Automatic classification Chinese poetry Poetry (Literary form) sug: subj: Artificial neural networks Deep learning Convolutional neural networks Automatic classification Chinese poetry Poetry (Literary form) keyword: Chang'an deep learning image Tang poetry classification ab: 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 (应制), emotional poetry (感怀), parting poetry (离别), and other poetry (其他). 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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