Hierarchical text categorization using neural networks
Hierarchical text categorization using neural networks
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DOI：
10.1023/a:1012782908347
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发表时间：
2002-01-01
期刊：
影响因子：
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通讯作者：
Srinivasan, P
中科院分区：
文献类型：
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作者：
Ruiz, ME;Srinivasan, P
本文提出了一种基于分层专家混合模型的文本分类方法的设计和评价。该模型使用分而治之的原则来定义较小的分类问题的基础上预定义的层次结构。最后的分类器是一个分层的神经网络阵列。该方法使用UMLS元词库作为底层层次结构进行评估，并使用MEDLINE记录的OHSUMED测试集。与传统的Rocchio算法的文本分类，以及平面神经网络分类器的优化版本的比较。结果表明，使用的层次结构提高了文本分类性能相对于一个等效的平面模型。优化后的Rocchio算法实现了与分层神经网络的性能相媲美。
This paper presents the design and evaluation of a text categorization method based on the Hierarchical Mixture of Experts model. This model uses a divide and conquer principle to define smaller categorization problems based on a predefined hierarchical structure. The final classifier is a hierarchical array of neural networks. The method is evaluated using the UMLS Metathesaurus as the underlying hierarchical structure, and the OHSUMED test set of MEDLINE records. Comparisons with an optimized version of the traditional Rocchio's algorithm adapted for text categorization, as well as flat neural network classifiers are provided. The results show that the use of the hierarchical structure improves text categorization performance with respect to an equivalent flat model. The optimized Rocchio algorithm achieves a performance comparable with that of the hierarchical neural networks.