Acta Scientiarum Naturalium Universitatis Pekinensis

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Automatic Classification of Tang Poetry Themes

HU Renfen1, ZHU Yuchen2   

  1. 1. Institute of Chinese Information Processing, Beijing Normal University, Beijing 100875; 2. School of Chinese Language and Literature, Beijing Normal University, Beijing 100875;
  • Received:2014-07-27 Online:2015-03-20 Published:2015-03-20



  1. 1. 北京师范大学中文信息处理研究所, 北京 100875; 2. 北京师范大学文学院, 北京 100875;

Abstract: The authors propose a text classification model for Tang poetry. Firstly seven categories are defined for poetry themes: love and marriage, frontier war, friendship and farewell, journey and homesick, landscape and countryside, history and nostalgia, others. 500 Tang poems are selected as research samples, and they are represented in vectors with Vector Space Model (VSM). To reduce the vector dimensions, feature selection is made by Chi-square test. Two classifiers are built based on Naive Bayes and Support Vector Machine algorithms. The models perform well in classification experiment. Besides, the authors verify the positive effect of poetry titles, authors and types to poetry themes by text classification models, which could offer scientific reference to the related research of Tang poetry.

Key words: Tang poetry, themes, text classification, Chi-square test, Naive Bayes, support vector machine

摘要: 将文本分类技术引入唐诗研究。首先将唐诗按照题材分为爱情婚姻、边塞战争、交游送别、羁旅思乡、山水田园、咏史怀古和其他7类, 并据此提出唐诗题材自动分类模型。所选500首诗歌样本以《唐诗三百首》为基础, 并有所补充。采用向量空间模型(VSM)将唐诗文本转换为向量, 通过卡方检验进行词语特征选择, 最后基于朴素贝叶斯和支持向量机算法构造文本分类器, 取得较好的题材分类效果。此外, 还验证了作者关于题目、体制、作者等变量对题材分类产生影响的假设, 为相关诗歌本体研究提供了科学依据。

关键词: 唐诗, 题材, 文本分类, 卡方检验, 朴素贝叶斯, 支持向量机

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