@conference {Gueddah2016,
	title = {The filtered combination of the weighted edit distance and the Jaro-Winkler distance to improve spellchecking Arabic texts},
	booktitle = {Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA},
	volume = {2016-July},
	year = {2016},
	note = {cited By 0},
	abstract = {The digital environments for human learning have been much evolving thanks to the incredible progress of information technologies. This is particularly the case for automatic correction of spelling errors requested by a large majority of people nowadays. Almost all of the current spellcheckers are semiautomatic, and they enable users to find the good solution for a committed error. The major shortcoming of the existing metric methods of correction lies in the bad scheduling of the solutions suggested to the spellchecking out of context of a detected error. To overcome this limitation, we have developed several approaches which suggest probability costs estimated from a learning test. It is attributed in various editing operations during calculating measure of similarity, case of the edit distance. The idea developed in this work was to know how to efficiently weigh these editing operations without resorting to a phase of learning. This is based only on the proximity and the similarity between Arabic keyboard keys. Additionally, we have suggested combining this measure with the distance of Jaro-Winkler in order to better filter, refine and weigh certain solutions compared to others. The experimental results stem from tests conducted on errors committed in a learning corpus, trying to validate the choices of conception and to prove the interest of both approaches. {\textcopyright} 2015 IEEE.},
	doi = {10.1109/AICCSA.2015.7507128},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84980349949\&doi=10.1109\%2fAICCSA.2015.7507128\&partnerID=40\&md5=4274ec18faa9128a9efb4ef1c135fe25},
	author = {Gueddah, H.a and Yousfi, A.b and Belkasmi, M.a}
}
@article {Nejja2016131,
	title = {A lightweight system for correction of Arabic derived words},
	journal = {Lecture Notes in Electrical Engineering},
	volume = {380},
	year = {2016},
	note = {cited By 0},
	pages = {131-138},
	abstract = {In this paper, we address the lexicon insufficient problem used in the automatic spell checker. In order to solve that deficiency, we developed an approach that aims to correct the derived words, considering that the most Arabic words are derived ones by adjusting the Levenshtein algorithm to our need. This method is based on a corpus constituted of surface patterns and roots characterized by a scaled down size compared to conventional approaches The proposed method reduced the execution time while maintaining the highest correction rate. {\textcopyright} Springer International Publishing Switzerland 2016.},
	doi = {10.1007/978-3-319-30301-7_14},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964556163\&doi=10.1007\%2f978-3-319-30301-7_14\&partnerID=40\&md5=3031a40b3d60aeae121187c174e48ccf},
	author = {Nejja, M.a and Yousfi, A.b}
}
@conference {Nejja2015109,
	title = {The Context in Automatic Spell Correction},
	booktitle = {Procedia Computer Science},
	volume = {73},
	year = {2015},
	note = {cited By 0},
	pages = {109-114},
	abstract = {Automatic spell checker systems aim to verify and correct erroneous words through a suggested set of words that are the nearest lexically to the erroneous ones. However, the main disadvantage of those systems is that the wanted solution may bethe most distant lexically but the most appropriate in context. To correctly sort these solutions, the corrector must have some information about the general context of the text; the paragraph or the neighboring words of the misspelled word. In this paper, we will present an automatic spell checking method according to the context. This method is based on a learning corpus containing a distribution of the appearance probability of a word in different contexts. It combines the lexical correction via the Levenstein algorithm and the context based correction through a well definedlearning corpus which is composed of documents collected from the internet. This approach has proven efficiency and the obtained results are much more improved and satisfactory. {\textcopyright} 2015 The Authors.},
	doi = {10.1016/j.procs.2015.12.055},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962653409\&doi=10.1016\%2fj.procs.2015.12.055\&partnerID=40\&md5=de57303c462c687920cca58f379ceaef},
	author = {Nejja, M.a and Yousfi, A.b}
}
@conference {Gueddah2013,
	title = {The impact of arabic inter-character proximity and similarity on spell-checking},
	booktitle = {2013 8th International Conference on Intelligent Systems: Theories and Applications, SITA 2013},
	year = {2013},
	note = {cited By 0},
	abstract = {Following a statistical study carried out on the typographical errors committed when typing documents in Arabic language, it was found that most of these typos are character permutation errors, accounting for 65\% of overall errors. {\textcopyright} 2013 IEEE.},
	doi = {10.1109/SITA.2013.6560811},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883129467\&doi=10.1109\%2fSITA.2013.6560811\&partnerID=40\&md5=a0c5fdf32bbc85908946854321fb8cd0},
	author = {Gueddah, H.a and Yousfi, A.b}
}
