基于朴素贝叶斯的文本分类研究综述

被引：252

作者：

贺鸣

孙建军

成颖

机构：

[1] 南京大学信息管理学院

来源：

情报科学 | 2016年 / 34卷 / 07期

关键词：

自动分类; 朴素贝叶斯; 特征选择; 特征过滤;

D O I：

暂无

中图分类号：

G254.1 [分类法];

学科分类号：

120501 [图书馆学];

摘要：

文本自动分类是自然语言处理领域的重要分支之一,已经形成了大量的模型以及算法,其中基于朴素贝叶斯的相关研究是该领域持续的热点。本文对基于朴素贝叶斯的文本自动分类研究进行了系统的综述。探讨了多项式模型和多元伯努利模型等经典的朴素贝叶斯分类方法。重点分析了经典的特征选择方法以及包括ALOFT等在内的多种改进的特征选择方法。论文还对从加权、避免平滑等视角的NB改进算法进行了梳理。最后,提出了进一步改进NB的主要思路。

引用

页码：147 / 154

页数：8

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