Improving the classification accuracy of automatic text processing systems using context vectors and back-propagation algorithms

J. Farkas
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引用次数: 7

Abstract

We analyze some of the benefits of combining the context-vector representation of documents with the back-propagation paradigm for document classification. We discuss an implementation of this architecture, called NeuroFile, which combines automatic document classification with similarity-based, as well as Boolean retrieval facilities in a single electronic filing system. The quality of performance of NeuroFile is compared with an earlier system called NeuroClass. We show that NeuroFile achieves a 9% classification improvement over NeuroClass.
利用上下文向量和反向传播算法提高自动文本处理系统的分类精度
我们分析了将文档的上下文向量表示与文档分类的反向传播范式相结合的一些好处。我们将讨论该体系结构的一个实现,称为NeuroFile,它将自动文档分类与基于相似性的方法以及布尔检索工具结合在一个电子文件系统中。NeuroFile的性能质量与早期的系统NeuroClass进行了比较。我们表明NeuroFile比NeuroClass实现了9%的分类改进。
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