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Multimedia Short Text Classification via Deep RNN-CNN Cascade

Submit Time: 2019-02-22
Author: 陶爱山 1 ;
Institute: 1.同济大学;


Abstract—With the rapid development of mobile technologies, social networking softwares such as Twitter, Weibo and WeChat are becoming ubiquitous in our every day life. These social networks generate a deluge of data that consists of not only plain texts but also images, videos, and audios. As a consequence, the traditional approaches that classify the short text by counting only the key words become inadequate. In this paper, we propose a multimedia short text classification approach by deep RNN(Recurrent neural network ) and CNN(Convolutional neural network) cascade. We first employ an LSTM(Long short-term memory) net- work to convert the information in the images into text information. Then a convolutional neural network is used to classify the multimedia texts by taking into account both the texts generated from the image as well as those contained in the initial message. It is seen through experiments using MSCOCO dataset that the proposed method exhibits significant performance improvement over the traditional methods.
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From: 陶爱山
Keywords: LSTM,RNN,CNN;
Recommended references: 陶爱山.(2019).Multimedia Short Text Classification via Deep RNN-CNN Cascade.[ChinaXiv:201902.00062] (Click&Copy)
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[V1] 2019-02-22 21:12:21 chinaXiv:201902.00062V1 Download
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