International Journal For Multidisciplinary Research

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

Author(s) Chunduri Sai Ganesh, Dasari Leela Venkata Kondala Rao, E Srividhya
Country India
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.
Keywords auto-correct, test feature analysis, natural language processing, spelling errors, grammatical accuracy, NLP techniques, context analysis, error identification, suggestion generation, test features, word frequency, word length, part-of-speech
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-03-12
Cite This Autocorrect of a Word in a Sentence using Text Feature Analysis with Natural Language Processing - Chunduri Sai Ganesh, Dasari Leela Venkata Kondala Rao, E Srividhya - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.13803
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.13803
Short DOI https://doi.org/gtmzv3
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E-ISSN 2582-2160
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