\n\t

\n\t\tDensely Connected CNN with Multi-scale Feature Attention for Text Classification\n\t<\/p>\n\t

\n\t\tShiyao Wang, Minlie Huang, Zhidong Deng\n\t<\/p>\n\t

\n\t\tAbstract\n\t<\/p>\n\t

\n\t\tText classification is a fundamental problem in natural language processing. As a popular deep learning model, convolutional neural net- work(CNN) has demonstrated great success in this task. However, most existing CNN models apply convolution filters of fixed window size, thereby unable to learn variable n-gram features flexibly. In this paper, we present a densely connected CNN with multi-scale feature attention for text classification. The dense connections build short-cut paths between upstream and downstream convolutional blocks, which enable the model to compose features of larger scale from those of smaller scale, and thus produce variable n-gram features. Furthermore, a multi-scale feature atten- tion is developed to adaptively select multi-scale features for classification. Extensive experiments demonstrate that our model obtains competitive performance against state-of-the-art baselines on six benchmark datasets. Attention visualization further reveals the model\u2019s ability to select proper n-gram features for text classification. Our code is available at: https:\/\/github.com\/wangs hy31\/Densely-Connected-CNN-with-Multiscale-Feature-Attention.git.\n\t<\/p>\n<\/div>\n\n\n\n

Densely-Connected-CNN-with-Multi-scale-Feature-Attention-for-Text-Classification<\/a>\u4e0b\u8f7d<\/a><\/div>\n\n\n\n

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INTRO<\/h3>\n\n\n\n In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his\/her related research with you.<\/p>\n\n\n\n

INTRO<\/h3>\n\n\n\n
In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his\/her related research with you.<\/p>\n\n\n\n