A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is
Text classification is a fundamental problem in natural language processing. As a popular deep learning model, convolutional neural network (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 betweendoi:10.24963/ijcai.2018/621 dblp:conf/ijcai/WangHD18 fatcat:zx6aa5sx25d2pablints6c7gce