# What Does a TextCNN Learn?

arXiv: https://arxiv.org/abs/1801.06287 · HTML: https://mlsanity.com/paper/1801.06287/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2018.

## Built on by (most cited first, 3 of 3)

- [Recent Advances and Trends in Multimodal Deep Learning: A Review](https://mlsanity.com/paper/2105.11087/) (2021)
- [An Explainable CNN Approach for Medical Codes Prediction from Clinical Text](https://mlsanity.com/paper/2101.11430/) (2021)
- [WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking](https://mlsanity.com/paper/2411.09820/) (2024)

## Abstract (arXiv)

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.