Text classification with pixel embedding

11/11/2019
by   Bin Liu, et al.
0

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.

READ FULL TEXT
research
06/29/2020

Combine Convolution with Recurrent Networks for Text Classification

Convolutional neural network (CNN) and recurrent neural network (RNN) ar...
research
03/10/2022

TextConvoNet:A Convolutional Neural Network based Architecture for Text Classification

In recent years, deep learning-based models have significantly improved ...
research
10/14/2019

Interpretable Text Classification Using CNN and Max-pooling

Deep neural networks have been widely used in text classification. Howev...
research
06/29/2020

Multichannel CNN with Attention for Text Classification

Recent years, the approaches based on neural networks have shown remarka...
research
07/23/2018

Text Classification based on Multiple Block Convolutional Highways

In the Text Classification areas of Sentiment Analysis, Subjectivity/Obj...
research
11/21/2016

Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling

Recurrent Neural Network (RNN) is one of the most popular architectures ...
research
06/24/2022

A multi-model-based deep learning framework for short text multiclass classification with the imbalanced and extremely small data set

Text classification plays an important role in many practical applicatio...

Please sign up or login with your details

Forgot password? Click here to reset