Character-level Convolutional Network for Text Classification Applied to Chinese Corpus

11/14/2016
by   Weijie Huang, et al.
0

This article provides an interesting exploration of character-level convolutional neural network solving Chinese corpus text classification problem. We constructed a large-scale Chinese language dataset, and the result shows that character-level convolutional neural network works better on Chinese corpus than its corresponding pinyin format dataset. This is the first time that character-level convolutional neural network applied to text classification problem.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/04/2015

Character-level Convolutional Networks for Text Classification

This article offers an empirical exploration on the use of character-lev...
research
01/13/2021

geoGAT: Graph Model Based on Attention Mechanism for Geographic Text Classification

In the area of geographic information processing. There are few research...
research
11/09/2020

Text Classification through Glyph-aware Disentangled Character Embedding and Semantic Sub-character Augmentation

We propose a new character-based text classification framework for non-a...
research
03/18/2019

A Multilingual Encoding Method for Text Classification and Dialect Identification Using Convolutional Neural Network

This thesis presents a language-independent text classification model by...
research
03/11/2019

An Innovative Word Encoding Method For Text Classification Using Convolutional Neural Network

Text classification plays a vital role today especially with the intensi...
research
12/09/2022

Moto: Enhancing Embedding with Multiple Joint Factors for Chinese Text Classification

Recently, language representation techniques have achieved great perform...
research
08/08/2017

Which Encoding is the Best for Text Classification in Chinese, English, Japanese and Korean?

This article offers an empirical study on the different ways of encoding...

Please sign up or login with your details

Forgot password? Click here to reset