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Research on development of retrieval and classification system of documents on the Web which is based on analysis of teaching text books.

基于教材分析的网络文献检索与分类系统开发研究

基本信息

批准号：
13558012
负责人：
NAKAYAMA Minoru
金额：
$ 5.89万
依托单位：
TOKYO INSTITUTE OF TECHNOLOGY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2001
资助国家：
日本
起止时间：
2001 至 2003
项目状态：
已结题

项目摘要

The purpose of this study is to compare performance among developed subject classification methods for educational Web re-sources.Three methods are vector space model (VSM) using degree of cosine similarity, vector distance on the self-organizing map (SOM) and three layer percptron (MLP). The document vectors were estimated by the term feature vectors which were extracted from the teaching guidelines based on the singular value decomposition method (SVD).The 403 teaching schemes were prepared as the test documents for the subject discrimination. The recall rate, precision rate and Fl measure as the classification performance measure, were compared across three methods. The recall rate using MLP is the highest for most subjects. The highest precision rate among the methods depend on subject. As a result, F1 measure as total performance index of most subjects are the highest for the method using MLP.According to the results, the subject classification methods for educational Web resources by using multi layer perceptron (MLP) is tested to achieye better performance. Ten classification methods and 403 test documents were prepared, then classification perfor-mance was compared among conditions by the F1 measure.As a result, it was examined effectiveness of singular value decomposition (SVD) to extract term feature from term-document matrix as description characteristics of the national teaching guideline. The highest performance of classification method was the use of MLP which trained with all term features extracting from SVD when document feature was estimated by terms appering unique subject.Also, it was suggested that there were significant differences of document feature components between correct and incorrect classified documents.

本研究的目的是比较三种已发展的教育网站资源主题分类方法的效能，分别是向量空间模型（VSM）、向量距离自组织映射（SOM）和三层自组织映射（MLP）。基于奇异值分解（SVD）方法提取教学大纲中的术语特征向量，对文档向量进行估计，并以403份教学大纲为测试文档进行主题判别。比较了三种方法的查全率、查准率和分类性能指标Fl。对于大多数受试者，使用MLP的召回率最高。在这些方法中，最高的准确率取决于主题。结果表明，采用多层感知器（MLP）的分类方法在大多数主题的总体性能指标F1中最高，并以此为依据，对采用多层感知器（MLP）的教育Web资源主题分类方法进行了测试，取得了较好的分类效果。选取10种分类方法和403篇测试文档，通过F1测度比较不同分类条件下的分类性能，验证了奇异值分解（SVD）提取术语-文档矩阵特征作为国家教学大纲描述特征的有效性。当文档特征由唯一主题词估计时，使用SVD提取的所有特征词训练的MLP分类方法的分类性能最高，并且发现正确分类文档与错误分类文档的文档特征成分存在显著差异。