不同情境下中文文本分类模型的表现及选择

Performance and Choice of Chinese Text Classification Models in Different Situations

针对中文文本分类任务中N-Gram,素贝叶斯、K最近邻和TF-IDF等经典而广泛使用的文本分类模型的选择困惑问题,基于万余篇中文新闻文本语料数据,设计了一系列的对比实验,考察了各模型在不同参数、不同训练数据规模、不同训练文本长度、类别是否偏斜等多种情境下分类性能的表现,总结了各模型的特性,为中文文本分类模型的选择和应用提供了实践依据和参考.

N-Gram,Nave Bayes,K nearest neighbors and TF-IDF are classical text classification models with a wide range of applications.People are often puzzled about which classification model should be used in a certain Chinese text classification task.This paper collected more than ten thousand Chinese news texts,and designed a series of experiments to analyze the performance of these models in varied situations from classification parameters,training data scale,text length and skewed data sets.The characteristics of these models were summarized,which provides a practical guide for the model selection in Chinese text classification tasks.