结合相关类别信息的大规模文本层次分类研究  

Research on large-scale text hierarchies combining relevant category information

作　　者：何世柱[1] 王明文[1] 周军军[1] 石松[1] 

机构地区：[1]江西师范大学计算机信息工程学院,江西南昌330022

出　　处：《山东大学学报（理学版）》2011年第5期58-62,共5页Journal of Shandong University(Natural Science)

基　　金：国家自然科学基金资助项目(60963014);江西省自然科学基金资助项目(2008GZS0052)

摘　　要：深层分类模型是一种解决大规模文本层次分类问题的有效范式。本文基于该范式提出一种改进型模型,首先将一种新方法用于单独评价搜索阶段的效果;然后利用类别和文档信息共同选择候选类别;最后基于类中心训练Rocchio分类器,同时利用相关类别的分类结果确定最终类别。在ODP数据集上的实验表明,相对于最新型的深层分类方法,该模型具有一定优势。The deep classification model is an effective paradigm for solving large-scale classification problems.An improved model was proposed based on the paradigm.First,a new method was used to evaluate the effectiveness of search stage independently.Second,the category and document information were collectively used to select category candidates.Finally,the classifier of Rocchio was trained based on the class centroid,and at the same time the information of related categories was used to determine the final category.Experiments on the corpus ODP show that the proposed approach outperforms the other new methods.

关 键 词：深层分类 大规模层次 分层分类 ROCCHIO 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]