A hybrid approach to automatic Chinese text checking and error correction

2002 ◽  
Author(s):  
Fuji Ren ◽  
Hongchi Shi ◽  
Qiang Zhou
Keyword(s):  
Error Correction ◽  
Chinese Text ◽  
Hybrid Approach

scholarly journals RACAI GEC – A hybrid approach to Grammatical Error Correction

2014 ◽  
Author(s):  
Tiberiu Boroş ◽  
Stefan Daniel Dumitrescu ◽  
Adrian Zafiu ◽  
Verginica Barbu Mititelu ◽  
Ionut Paul Vaduva
Keyword(s):  
Error Correction ◽  
Hybrid Approach ◽  
Grammatical Error

Error Correction Modelling of Wind Speed Through Hydro-Meteorological Parameters and Mesoscale Model: A Hybrid Approach

2012 ◽  
Vol 27 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Asnor Muizan Ishak ◽  
Renji Remesan ◽  
Prashant K. Srivastava ◽  
Tanvir Islam ◽  
Dawei Han
Keyword(s):  
Wind Speed ◽  
Error Correction ◽  
Meteorological Parameters ◽  
Mesoscale Model ◽  
Hybrid Approach

scholarly journals Long-read error correction: a survey and qualitative comparison

2020 ◽  
Author(s):  
Pierre Morisse ◽  
Thierry Lecroq ◽  
Arnaud Lefebvre
Keyword(s):  
Error Correction ◽  
Second Generation ◽  
Hybrid Approach ◽  
Error Rates ◽  
Sequencing Technology ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Long Read ◽  
Read Error Correction ◽  
Generation Sequencing

AbstractThird generation sequencing technologies Pacific Biosciences and Oxford Nanopore Technologies were respectively made available in 2011 and 2014. In contrast with second generation sequencing technologies such as Illumina, these new technologies allow the sequencing of long reads of tens to hundreds of kbps. These so called long reads are particularly promising, and are especially expected to solve various problems such as contig and haplotype assembly or scaffolding, for instance. However, these reads are also much more error prone than second generation reads, and display error rates reaching 10 to 30%, according to the sequencing technology and to the version of the chemistry. Moreover, these errors are mainly composed of insertions and deletions, whereas most errors are substitutions in Illumina reads. As a result, long reads require efficient error correction, and a plethora of error correction tools, directly targeted at these reads, were developed in the past nine years. These methods can adopt a hybrid approach, using complementary short reads to perform correction, or a self-correction approach, only making use of the information contained in the long reads sequences. Both these approaches make use of various strategies such as multiple sequence alignment, de Bruijn graphs, hidden Markov models, or even combine different strategies. In this paper, we describe a complete survey of long-read error correction, reviewing all the different methodologies and tools existing up to date, for both hybrid and self-correction. Moreover, the long reads characteristics, such as sequencing depth, length, error rate, or even sequencing technology, can have an impact on how well a given tool or strategy performs, and can thus drastically reduce the correction quality. We thus also present an in-depth benchmark of available long-read error correction tools, on a wide variety of datasets, composed of both simulated and real data, with various error rates, coverages, and read lengths, ranging from small bacterial to large mammal genomes.

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