Abstract:
Automatic spelling correction is a very important task used in many Natural Language Processing (NLP) applications such
as Optical Character Recognition (OCR), Information retrieval, etc. There are many approaches able to detect and correct misspelled
words. These approaches can be divided into two main categories: contextual and context-free approaches. In this paper, we propose a
new contextual spelling correction method applied to the Arabic language, without loss of generality for other languages. The method
is based on both the Viterbi algorithm and a probabilistic model built with a new estimate of n-gram language models combined
with the edit distance. The probabilistic model is learned with an Arabic multipurpose corpus. The originality of our work consists
in handling up global and simultaneous correction of a set of many erroneous words within sentences. The experiments carried out
prove the performance of our proposal, giving encouraging results for the correction of several spelling errors in a given context. The
method achieves a correction accuracy of up to 93.6% by evaluating the first given correction suggestion. It is able to take into account
strong links between distant words carrying meaning in a given context. The high-level correction accuracy of our method allows for
its integration into many applications.
