Text classification with pixel embedding

TL;DR

This paper introduces a novel text classification framework that converts text into 3D tensors of word images, enabling effective use of 3D CNNs for improved classification accuracy.

Contribution

It proposes transforming text into video-like 3D tensors of word images to facilitate convolutional n-gram modeling with CNNs, showing superior performance.

Findings

Outperforms existing text classification methods

Effective n-gram modeling with 3D CNNs

Robust across multiple datasets

Abstract

We propose a novel framework to understand the text by converting sentences or articles into video-like 3-dimensional tensors. Each frame, corresponding to a slice of the tensor, is a word image that is rendered by the word's shape. The length of the tensor equals to the number of words in the sentence or article. The proposed transformation from the text to a 3-dimensional tensor makes it very convenient to implement an $n$-gram model with convolutional neural networks for text analysis. Concretely, we impose a 3-dimensional convolutional kernel on the 3-dimensional text tensor. The first two dimensions of the convolutional kernel size equal the size of the word image and the last dimension of the kernel size is $n$. That is, every time when we slide the 3-dimensional kernel over a word sequence, the convolution covers $n$ word images and outputs a scalar. By iterating this process continuously for each $n$-gram along with the sentence or article with multiple kernels, we obtain a 2-dimensional feature map. A subsequent 1-dimensional max-over-time pooling is applied to this feature map, and three fully-connected layers are used for conducting text classification finally. Experiments of several text classification datasets demonstrate surprisingly superior performances using the proposed model in comparison with existing methods.