This is a python program for Contextual Spell checking ** Autocorrection using NLP**

Find out if a word is right spelled through Natural Language Processing algorithm

Getting Started

In this model, we are going to consider edit distance between every pair of words in a list containing the vocabulary. Basically, edit distance is a measure of minimum edits required to convert one word to another. This process of conversion includes steps like Delete,Replace,Switch and Insert on a pair of words.

In order to do tests the Dictionary was used to provide the vocabulary and word to train the model.

In this project, to reduce complexity, we would go for words that are 1 or 2 edit distance away. The goal of our model to produce the right output is to compute the probability of a word being correct, P(c/w) ,is probability of certain word w given that is is correct, P(w/c) , multiplied to probability of being correct in general, P(c) , divided by probability of that word appearing, P(w) .

Formula : 𝑃(𝑐|𝑤)=𝑃(𝑤|𝑐)×𝑃(𝑐)/𝑃(𝑤)

The method used above is called Bayes Theorem.

Prerequisites

In order to use this porject you will need Python 3 and some libraries described bellow

re numpy pandas Running

To run the project on your machine you will need to clone this repository and execute the python3 command on the main.py file, as described bellow step-by-step:

git clone cd python3 main.py Built With

Python 3.7.3 - One of the most high level programming languages used Pandas - Library of data manipulation and organization NumPy - Library to mathematical calculus on python algorithms