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Spelling Detection Errors Techniques in NLP: A Survey

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Rasha Altarawneh

Rasha Altarawneh. Spelling Detection Errors Techniques in NLP: A Survey. International Journal of Computer Applications 172(4):1-5, August 2017. BibTeX

	author = {Rasha Altarawneh},
	title = {Spelling Detection Errors Techniques in NLP: A Survey},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2017},
	volume = {172},
	number = {4},
	month = {Aug},
	year = {2017},
	issn = {0975-8887},
	pages = {1-5},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017915176},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


This paper reports the efforts of Arabic Spell-checker researches by providing a brief summary of proposed methods and techniques that explains how the spelling errors might be discovered in any entered text. It mainly unites two areas that are quite different in appearance, computer science and natural languages .The domain of this topic is limited because of the complex morphology it has compared with other Languages, and the variation of its stems and the similarity of the characters for this language. This poses a challenge for the researchers to concern about it.


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Spell-Checker , Natural Language, N-gram, Radix tree , Context text