Efficient Weighted Edit Distance and N-gram Language Models to Improve Spelling Correction of Segmentation Errors

In most research that has dealt with the correction of spelling errors, the errors are caused by the misuse of space (deletion or insertion of space) are not tackled. Forgetting to deal with this type of errors in the texts poses a problem of understanding and ambiguity of the meaning of the sentence containing these errors. In this article, we propose a new approach to correct errors due to the insertion of space in a word, and at the same time correct other types of editing errors. This approach is based on the edit distance and uses bi-grams language models to correct words in context. The test conducted on hundreds of erroneous words (by insertion of space and/or by simple editing errors) made it possible to assess the relevance and validity of the methods developed to correct this type of error. The approaches proposed in this article provide a very important clarification and reminder by comparing them to those of other existing approaches.

@article{GUEDDAH2021,
  title     = {Efficient Weighted Edit Distance and N-gram Language Models to Improve Spelling Correction of Segmentation Errors},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {12},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Hicham GUEDDAH},
  doi       = {10.14569/IJACSA.2021.01212113},
  url       = {https://doi.org/10.14569/IJACSA.2021.01212113}
}