TY - JOUR

T1 - Automatic Correction of Arabic Dyslexic Text

AU - Alamri, Maha

AU - Teahan, William

N1 - The Saudi Arabian government sponsored the PhD scholarship for Maha M. Alamri.

PY - 2019/2/21

Y1 - 2019/2/21

N2 - This paper proposes an automatic correction system that detects and corrects dyslexic errors in Arabic text. The system uses a language model based on the Prediction by Partial Matching (PPM) text compression scheme that generates possible alternatives for each misspelled word. Furthermore, the generated candidate list is based on edit operations (insertion, deletion, substitution and transposition), and the correct alternative for each misspelled word is chosen on the basis of the compression codelength of the trigram. The system is compared with widely-used Arabic word processing software and the Farasa tool. The system provided good results compared with the other tools, with a recall of 43%, precision 89%, F1 58% and accuracy 81%.

AB - This paper proposes an automatic correction system that detects and corrects dyslexic errors in Arabic text. The system uses a language model based on the Prediction by Partial Matching (PPM) text compression scheme that generates possible alternatives for each misspelled word. Furthermore, the generated candidate list is based on edit operations (insertion, deletion, substitution and transposition), and the correct alternative for each misspelled word is chosen on the basis of the compression codelength of the trigram. The system is compared with widely-used Arabic word processing software and the Farasa tool. The system provided good results compared with the other tools, with a recall of 43%, precision 89%, F1 58% and accuracy 81%.

KW - dyslexia

KW - natural language processing

KW - automatic spelling correction for Arabic

U2 - 10.3390/computers8010019

DO - 10.3390/computers8010019

M3 - Article

VL - 8

JO - Computers

JF - Computers

SN - 2073-431X

IS - 1

M1 - 19

ER -