Mongolian Spelling Error Correction Using Word NGram Method

2021 ◽  
pp. 94-101
Author(s):  
Byambasuren Ivanov ◽  
Markhamet Musa ◽  
Uuganbaatar Dulamragchaa
Keyword(s):  
Error Correction ◽  
Spelling Error

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

The Utilization of Local Document Information to Improve Statistical Context-Sensitive Spelling Error Correction

2017 ◽  
Vol 23 (7) ◽  
pp. 446-451
Author(s):  
Jung-Hun Lee ◽  
Minho Kim ◽  
Hyuk-Chul Kwon
Keyword(s):  
Error Correction ◽  
Spelling Error ◽  
Context Sensitive

scholarly journals Global Attention Decoder for Chinese Spelling Error Correction

2021 ◽  
Author(s):  
Zhao Guo ◽  
Yuan Ni ◽  
Keqiang Wang ◽  
Wei Zhu ◽  
Guotong Xie
Keyword(s):  
Error Correction ◽  
Spelling Error

scholarly journals Spelling Error Correction with Soft-Masked BERT

2020 ◽  
Author(s):  
Shaohua Zhang ◽  
Haoran Huang ◽  
Jicong Liu ◽  
Hang Li
Keyword(s):  
Error Correction ◽  
Spelling Error

Generalized Character-Level Spelling Error Correction

2014 ◽  
Author(s):  
Noura Farra ◽  
Nadi Tomeh ◽  
Alla Rozovskaya ◽  
Nizar Habash
Keyword(s):  
Error Correction ◽  
Spelling Error

scholarly journals Adaptive Spelling Error Correction Models for Learner English

2017 ◽  
Vol 112 ◽  
pp. 474-483 ◽  
Author(s):  
Ryo Nagata ◽  
Hiroya Takamura ◽  
Graham Neubig
Keyword(s):  
Error Correction ◽  
Spelling Error ◽  
Error Correction Models

scholarly journals Morphological processing of Indian languages for lexical interaction with application to spelling error correction

Sadhana ◽  
1996 ◽  
Vol 21 (3) ◽  
pp. 363-380 ◽  
Author(s):  
P Sengupta ◽  
B B Chaudhuri
Keyword(s):  
Error Correction ◽  
Morphological Processing ◽  
Spelling Error ◽  
Indian Languages

scholarly journals Think Twice: A Post-Processing Approach for the Chinese Spelling Error Correction

2021 ◽  
Vol 11 (13) ◽  
pp. 5832
Author(s):  
Wei Gou ◽  
Zheng Chen
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Error Correction ◽  
Language Processing ◽  
Classification Model ◽  
Spelling Error ◽  
Learning Method ◽  
Post Processing ◽  
Rule Based ◽  
Model Training

Chinese Spelling Error Correction is a hot subject in the field of natural language processing. Researchers have already produced many great solutions, from the initial rule-based solution to the current deep learning method. At present, SpellGCN, proposed by Alibaba’s team, achieves the best results of which character level precision over SIGHAN2013 is 98.4%. However, when we apply this algorithm to practical error correction tasks, it produces many false error correction results. We believe that this is because the corpus used for model training contains significantly more errors than the text used for model correcting. In response to this problem, we propose performing a post-processing operation on the error correction tasks. We employ the initial model’s output as a candidate character, obtain various features of the character itself and its context, and then use a classification model to filter the initial model’s false error correction results. The post-processing idea introduced in this paper can apply to most Chinese Spelling Error Correction models to improve their performance over practical error correction tasks.

Vietnamese Context-Sensitive Malicious Spelling Error Correction

2020 ◽  
Author(s):  
Linh Thuy Nguyen ◽  
Ban Phuoc Dao ◽  
Duc-Vu Nguyen ◽  
Ngan Luu-Thuy Nguyen
Keyword(s):  
Error Correction ◽  
Spelling Error ◽  
Context Sensitive
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