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.
