TextCNN, the convolutional neural network for text, is a useful deep learning algorithm for sentence classification tasks such as sentiment analysis and question classification. However, neural networks have long been known as black boxes because interpreting them is a challenging task. Researchers have developed several tools to understand a CNN for image classification by deep visualization, but research about deep TextCNNs is still insufficient. In this paper, we are trying to understand what a TextCNN learns on two classical NLP datasets. Our work focuses on functions of different convolutional kernels and correlations between convolutional kernels.

翻译：TextCNN是文本的进化神经网络,对于情绪分析和问题分类等刑罚分类任务来说,是一种有用的深层次学习算法。然而,神经网络长期以来一直被称为黑盒,因为解释这些网络是一项艰巨的任务。研究人员开发了几种工具来理解有线电视新闻网,以便通过深可见化进行图像分类,但关于深层TextCNNs的研究仍然不够充分。在本文中,我们试图了解一个TextCNN在两个古典NLP数据集中学到了什么。我们的工作侧重于不同的进化内核的功能以及进化内核之间的相互关系。