Apr 21, 2020 · Therefore, a shuffle convolution neural network (SCNN) is proposed to address the shallow learning problem by introducing wider inception cell ...
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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 ...
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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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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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 ...
Duration: 16:28
Posted: Dec 6, 2018
Posted: Dec 6, 2018
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, ...
[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.
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Encouraged by these results, we pro- vide an extensive empirical evaluation of CNNs on large- scale video classification using a new dataset of 1 million.
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Online Text Classification — An all-in-one solution for AI and ML training data with labeling, data curation, and more. Supercharge how you build intelligent...
For AI Developers, by AI Developers. A true end-to-end platform for all AI workloads. Ease building, deploying and...