%0 Journal Article
%T 不同情境下中文文本分类模型的表现及选择
%A 兰秋军
%A 李卫康
%A 刘文星
%J 湖南大学学报(自然科学版)
%D 2016
%X 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.</br>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.
%K 中文文本
%K 文本分类
%K 数据挖掘 情报分析&lt
%K /br&gt
%K Chinese text text classification data mining information analysis
%U http://hdxbzkb.cnjournals.net/ch/reader/view_abstract.aspx?file_no=20160419&flag=1