Improving Multilabel Text Classification with Stacking and Recurrent Neural Networks

R. M. Nunes, M. A. Domingues, V. D. Feltrim
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引用次数: 1

Abstract

Multilabel text classification can be defined as a mapping function that categorizes a text in natural language into one or more labels defined by the scope of a problem. In this work we propose an architecture of stacked classifiers for multilabel text classification. The proposed models use an LSTM recurrent neural network in the first stage of the stack and different multilabel classifiers in the second stage. We evaluated our proposal in two datasets well-known in the literature (TMDB and EUR-LEX Subject Matters), and the results showed that the proposed stack consistently outperforms the baselines.
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用堆叠和递归神经网络改进多标签文本分类
多标签文本分类可以定义为一种映射函数,它将自然语言中的文本分类为由问题范围定义的一个或多个标签。在这项工作中,我们提出了一种用于多标签文本分类的堆叠分类器架构。提出的模型在堆栈的第一阶段使用LSTM递归神经网络,在第二阶段使用不同的多标签分类器。我们在两个文献中知名的数据集(TMDB和EUR-LEX Subject Matters)中评估了我们的提议,结果表明提议的堆栈始终优于基线。
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