scholarly journals Deep Learning-Based Context-Sensitive Spelling Typing Error Correction

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 152565-152578
Author(s):  
Jung-Hun Lee ◽  
Minho Kim ◽  
Hyuk-Chul Kwon
Keyword(s):  
Deep Learning ◽  
Error Correction ◽  
Context Sensitive ◽  
Typing Error

Deep Learning Automated Background Phase Error Correction for Abdominopelvic 4D Flow MRI

Radiology ◽  
2021 ◽  
Author(s):  
Sophie You ◽  
Evan M. Masutani ◽  
Marcus T. Alley ◽  
Shreyas S. Vasanawala ◽  
Pam R. Taub ◽  
...  
Keyword(s):  
Deep Learning ◽  
Error Correction ◽  
Phase Error ◽  
4D Flow Mri ◽  
4D Flow ◽  
Flow Mri ◽  
Background Phase

Exploiting Error-Correction-CRC for Polar SCL Decoding: A Deep Learning-Based Approach

2020 ◽  
Vol 6 (2) ◽  
pp. 817-828 ◽  
Author(s):  
Xijin Liu ◽  
Shaohua Wu ◽  
Ye Wang ◽  
Ning Zhang ◽  
Jian Jiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Error Correction

Context-sensitive Spelling Error Correction using Eojeol N-gram

2014 ◽  
Vol 41 (12) ◽  
pp. 1081-1089 ◽  
Author(s):  
Minho Kim ◽  
Hyuk-Chul Kwon ◽  
Sungki Choi
Keyword(s):  
Error Correction ◽  
Spelling Error ◽  
Context Sensitive ◽  
N Gram

scholarly journals Design and Application of English Grammar Error Correction System Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chen Hongli
Keyword(s):  
Deep Learning ◽  
Error Correction ◽  
Language Model ◽  
Service Access ◽  
English Grammar ◽  
Grammatical Errors ◽  
Normal Sentence ◽  
Multilayer Feedforward Neural Network ◽  
Business Requirements ◽  
Correction System

In order to solve the problems of low correction accuracy and long correction time in the traditional English grammar error correction system, an English grammar error correction system based on deep learning is designed in this paper. This method analyzes the business requirements and functions of the English grammar error correction system and then designs the overall architecture of the system according to the analysis results, including English grammar error correction module, service access module, and feedback filtering module. The multilayer feedforward neural network is used to construct the language model to judge whether the language sequence is a normal sentence, so as to complete the correction of English grammatical errors. The experimental results show that the designed system has high accuracy and fast speed in correcting English grammatical errors.

scholarly journals Artificial Intelligence-Enabled and Period-Aware Forecasting COVID-19 Spread

2021 ◽  
Vol 26 (1) ◽  
pp. 47-57
Author(s):  
Paul Menounga Mbilong ◽  
Asmae Berhich ◽  
Imane Jebli ◽  
Asmae El Kassiri ◽  
Fatima-Zahra Belouadha
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Health Management ◽  
Negative Impact ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Time Lags ◽  
Context Sensitive ◽  
Learning Techniques

Coronavirus 2019 (COVID-19) has reached the stage of an international epidemic with a major socioeconomic negative impact. Considering the weakness of the healthy structure and the limited availability of test kits, particularly in emerging countries, predicting the spread of COVID-19 is expected to help decision-makers to improve health management and contribute to alleviating the related risks. In this article, we studied the effectiveness of machine learning techniques using Morocco as a case-study. We studied the performance of six multi-step models derived from both Machine Learning and Deep Learning regards multiple scenarios by combining different time lags and three COVID-19 datasets(periods): confinement, deconfinement, and hybrid datasets. The results prove the efficiency of Deep Learning models and identify the best combinations of these models and the time lags enabling good predictions of new cases. The results also show that the prediction of the spread of COVID-19 is a context sensitive problem.

Sign in / Sign up

Export Citation Format

Share Document