Autocorrect of a Word in a Sentence using Text Feature Analysis with Natural Language Processing

Abstract

In text editing software, auto-correct is a crucial tool that helps users correct spelling mistakes and improve the grammatical accuracy of their written content. The unique method for auto-correction proposed in this paper combines test feature analysis with natural language processing (NLP). The algorithm makes use of NLP techniques to examine the original sentence's context and spot any potential mistakes. After that, it runs a number of tests to produce correction suggestions for the word in question. This strategy is predicated on the idea that test characteristics like word frequency, word length, and part of speech can be utilized to distinguish between appropriate and inappropriate word patterns. The efficiency of the suggested strategy in correctly correcting words inside a phrase is shown by experimental findings.