SVM-BASED CLASSIFICATION METHOD FOR POETRY STYLE

摘要：

Bold-and-unconstrained style and Graceful-and-restrained style can characterize poetrys taste, which usually is judged personally, so the assessment is always subjective.If the methods of Machine Learning can be used to assess poetry style, it will be more objective.This paper brings forward a method based on Support Vector Machine (SVM for short) to differentiate bold-and-unconstrained style from graceful-and-restrained style of poetry.In this work, a piece of poetry is expressed using Vector Space Model (VSM for short) first, and then we use information gain to select the poetrys feature terms.At last, we use an SVM-based method to divide the style of poetry.Meanwhile, feature numbers and feature items for poetry styles influence are also analyzed.The performance of the proposed method has been evaluated by a series of experiments with interesting results.

关键词： Text classification Support vector machine Feature selection Poetry style

作者: ZHONG-SHI HE WEN-TING LIANG LIANG-YAN LI YU-FANG TIAN

作者单位: College of Computer Science, Chongqing University, Chongqing 400044, China;Institute of Language Cog College of Computer Science, Chongqing University, Chongqing 400044, China Institute of Language Cognition and Information Processing, Chongqing University College of Mathematics and Physics, Chongqing University

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 2936-2940

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）