MYANMAR AUTOMATIC SPELLING CORRECTION BASED ON N-GRAM MODEL

Abstract

Myanmar spelling correction intended for real-word errors and non-word errors. There are three main modules in this thesis. They are error detection,candidates generation, error correction. Dictionary look up method is used for detecting errors, Levenshtein Distance Algorithm is used for generating candidates and N-gram model is used for correcting errors. There can be human-generated misspellings which can be distinguished into three groups (i) Typographic Errors (Non-word error) (ii)Phonetic Errors (Cognitive error) (iii) Context Errors (Real word errors). This spelling correction can solve all of these three misspellings problem and the main contribution of this system is to solve the context errors using n-gram model in sentence level. Moreover, this spelling correction can solve the pali and patsint misspelling errors. Experimental results show that each of error types can be solved by this spelling correction. The general accuracy of all error types is greater than 85%. This system is implemented by using python programming language with Linux system.