Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification

Oscar Quispe, Alexander Ocsa, R. Coronado
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引用次数: 10

Abstract

The classification of a real text should not be necessarily treated as a binary or multi-class classification, since the text may belong to one or more labels. This type of problem is called multi-label classification. In this paper, we propose the use of latent semantic indexing to text representation, convolutional neural networks to feature extraction and a single multi layer perceptron for multi-label classification in real text data. The experiments show that the model outperforms state of the art techniques when the dataset has long documents, and we observe that the precision is poor when the size of the texts is small.
基于潜在语义索引和卷积神经网络的多标签多类文本分类
真实文本的分类不应该被视为二元或多类分类,因为文本可能属于一个或多个标签。这类问题被称为多标签分类。在本文中,我们提出使用潜在语义索引进行文本表示,使用卷积神经网络进行特征提取,并在真实文本数据中使用单层多层感知器进行多标签分类。实验表明,当数据集具有较长的文档时,该模型优于当前技术的状态,并且我们观察到当文本大小较小时,精度很差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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