@article {830,
	title = { Non-words Spell Corrector of Social Media Data in Message Filtering Systems {\textendash}},
	journal = {Journal of Digital Information Management},
	volume = {16},
	year = {2018},
	month = {04/2018},
	pages = {64-75},
	type = {Research},
	chapter = {64},
	abstract = {<p>We develop an extended version of spell checker and corrector to check non-word errors in social media datasets, which will be used in message filtering systems especially for cyberbullying detection. We use the dictionary techniques to check words, twelve-word spell error checking and correction approaches to correct the non-word errors, and n-gram and Levenshtein distance to select the most suitable word among corrected words. If there is more than one corrected word we get from each approach, we use n-gram techniques to choose the corrected and reasonable word from the words in n-gram database. When we used the Levenshtein distance in our previous work, we found that it selected the first corrected word and it was not a reasonable one in some sentences. Therefore, we use the n-gram database in this paper.</p>
},
	url = {http://dline.info/fpaper/jdim/v16i2/jdimv16i2_2.pdf},
	author = {Zar Zar Wint, and Th{\'e}o Ducros, and Masayoshi Aritsugi}
}
