Paper Info
Title
Image-based Natural Language Understanding Using 2D Convolutional Neural Networks.
Abstract
We propose a new approach to natural language understanding in which we consider the input text as an image and apply 2D Convolutional Neural Networks to learn the local and global semantics of the sentences from the variations ofthe visual patterns of words. Our approach demonstrates that it is possible to get semantically meaningful features from images with text without using optical character recognition and sequential processing pipelines, techniques that traditional Natural Language Understanding algorithms require. To validate our approach, we present results for two applications: text classification and dialog modeling. Using a 2D Convolutional Neural Network, we were able to outperform the state-of-art accuracy results of non-Latin alphabet-based text classification and achieved promising results for eight text classification datasets. Furthermore, our approach outperformed the memory networks when using out of vocabulary entities fromtask 4 of the bAbI dialog dataset.
Year
Venue
Field
2018
arXiv: Computation and Language
Dialog box,Convolutional neural network,Computer science,Optical character recognition,Image based,Natural language understanding,Artificial intelligence,Natural language processing,Semantics,Visual patterns,Alphabet
DocType
Volume
Citations 
Journal
abs/1810.10401
0
PageRank 
References 
Authors
0.34
16
11
Name
Order
Citations
PageRank
Erinc Merdivan1153.05
Anastasios Vafeiadis253.44
Dimitrios Kalatzis301.01
Sten Henke400.34
Johannes Kropf5297.04
Konstantinos Votis610936.15
Dimitrios Giakoumis701.35
Dimitrios Tzovaras81377205.82
Liming Chen92607201.71
Raouf Hamzaoui1052045.97
Matthieu Geist1138544.31