Latent semantic analysis for text categorization using neural network
Latent semantic analysis for text categorization using neural network
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DOI：
10.1016/j.knosys.2008.03.045
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发表时间：
2008-12
期刊：
影响因子：
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通讯作者：
Bo Yu;Zongben Xu;C. Li
中科院分区：
文献类型：
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作者：
Bo Yu;Zongben Xu;C. Li
提出了基于反向传播神经网络（BPNN）和改进反向传播神经网络（MBPNN）的文本分类模型。采用了一种有效的特征选择方法，在降低维数的同时提高了性能。由于基本的BPNN学习算法存在训练速度慢的缺点，因此我们对基本的BPNN学习算法进行了改进，以提高训练速度。分类精度也因此得到了提高。传统的基于词匹配的文本分类系统使用向量空间模型（VSM）来表示文档。然而，它需要一个高维空间来表示文档，并且没有考虑术语之间的语义关系，这也会导致分类精度差。潜在语义分析（LSA）可以克服使用统计派生的概念指标而不是单个单词所带来的问题。它构建了一个概念向量空间，其中每个术语或文档都表示为空间中的向量。它不仅大大降低了维数，而且发现了项之间重要的关联关系。我们在20个新闻组数据集上测试了我们的分类模型，实验结果表明使用MBPNN的模型优于基本的BPNN。在我们的系统中应用LSA可以在获得良好分类效果的同时实现大幅度的降维。
New text categorization models using back-propagation neural network (BPNN) and modified back-propagation neural network (MBPNN) are proposed. An efficient feature selection method is used to reduce the dimensionality as well as improve the performance. The basic BPNN learning algorithm has the drawback of slow training speed, so we modify the basic BPNN learning algorithm to accelerate the training speed. The categorization accuracy also has been improved consequently. Traditional word-matching based text categorization system uses vector space model (VSM) to represent the document. However, it needs a high dimensional space to represent the documents, and does not take into account the semantic relationship between terms, which can also lead to poor classification accuracy. Latent semantic analysis (LSA) can overcome the problems caused by using statistically derived conceptual indices instead of individual words. It constructs a conceptual vector space in which each term or document is represented as a vector in the space. It not only greatly reduces the dimensionality but also discovers the important associative relationship between terms. We test our categorization models on 20-newsgroup data set, experimental results show that the models using MBPNN outperform than the basic BPNN. And the application of LSA for our system can lead to dramatic dimensionality reduction while achieving good classification results.