Apr 21, 2020 · Therefore, a shuffle convolution neural network (SCNN) is proposed to address the shallow learning problem by introducing wider inception cell ...
Abstract: The dominant approaches for most natural language processing (NLP) tasks like text classification are recurrent neural networks (RNNs) and ...
Large-scale text classification with deeper and wider convolution neural network. 121. Thus, this paper attempts to apply deep CNNs to the. NLP problem. We ...
It can represent deeper local information of text data. We propose a large-scale scope-based convolutional neural network (LSS-CNN), which is based on scope ...
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Jiaying Wang et al.: Large-Scale Text Classification Using Scope-Based Convolutional Neural Network: A Deep Learning Approach. In the second stages, machine ...
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In this paper, we propose a graph-CNN based deep learning model to first convert texts to graph-of-words, and then use graph convolution operations to convolve ...
Abstract: In this paper we present an analysis on the usage of Deep Neural Networks for extreme multi-label and multi- class text classification.
[PDF] Very Deep Convolutional Networks for Text Classification - ACL Anthology
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Table 3: Large-scale text classification data sets used in our experiments. ... Deep convolutional neural networks for sentiment analysis of short texts. In ...
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Mar 10, 2022 · In this paper, we presented a convolutional neural network- based deep learning architecture for text classification. ... large-scale image ...
Deep neural networks have been widely used in text classification tasks since word order information can be utilized and more semantic features can be captured, ...
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