Persian Typographical Error Type Detection Using Deep Neural Networks on Algorithmically-Generated Misspellings

arXiv:2305.11731v5 Announce Type: replace
Abstract: Spelling correction is a remarkable challenge in the field of natural language processing. The objective of spelling correction tasks is to recognize and rectify spelling errors automatically. The development of applications that can effectually diagnose and correct Persian spelling and grammatical errors has become more important in order to improve the quality of Persian text. The Typographical Error Type Detection in Persian is a relatively understudied area. Therefore, this paper presents a compelling approach for …

abstract applications arxiv challenge cs.ai cs.cl detection development error errors generated language language processing natural natural language natural language processing networks neural networks processing tasks type