DeepAI AI Chat
Log In Sign Up

Seeing Colors: Learning Semantic Text Encoding for Classification

by   Shah Nawaz, et al.
SEECS Orientation

The question we answer with this work is: can we convert a text document into an image to exploit best image classification models to classify documents? To answer this question we present a novel text classification method which converts a text document into an encoded image, using word embedding and capabilities of Convolutional Neural Networks (CNNs), successfully employed in image classification. We evaluate our approach by obtaining promising results on some well-known benchmark datasets for text classification. This work allows the application of many of the advanced CNN architectures developed for Computer Vision to Natural Language Processing. We test the proposed approach on a multi-modal dataset, proving that it is possible to use a single deep model to represent text and image in the same feature space.


page 3

page 4

page 6


Image and Encoded Text Fusion for Multi-Modal Classification

Multi-modal approaches employ data from multiple input streams such as t...

In-depth Question classification using Convolutional Neural Networks

Convolutional neural networks for computer vision are fairly intuitive. ...

Doc2Im: document to image conversion through self-attentive embedding

Text classification is a fundamental task in NLP applications. Latest re...

Improving accuracy and speeding up Document Image Classification through parallel systems

This paper presents a study showing the benefits of the EfficientNet mod...

Deep Learning for Technical Document Classification

In large technology companies, the requirements for managing and organiz...

Convolutional Neural Networks for Toxic Comment Classification

Flood of information is produced in a daily basis through the global Int...