How to use levenshtein distance in text similarity in nlp

This recipe helps you use levenshtein distance in text similarity in nlp

Recipe Objective

How to use levenshtein distance in text similarity ?

levenshtein distance it is defined as distance in which less number of characters required to insert, delete or replace in a given string for e.g String 1 to transform it to another string which is String 2.

For e.g.

String A = helo

String B = hello

So in the above example we need to insert one missing character in String A which is l and transform it to String B. The Levenshtein distance for this will be 1 because there is only one edit is needed.

Similarly if:

String A = kelo

String B = hello

So in this the levenshtein distance will be 2, because not only insertion of l have to done but we have to substitute the character k by h.

Step 1 - Import the necessary libraries

import enchant

Step 2 - Define Sample strings

string_A = "helo" string_B = "hello"

Step 3 - Print the result for levenshtein Distance

print("The Levenshtein Distance between String_A and String_B is: ",enchant.utils.levenshtein(string_A, string_B))

The Levenshtein Distance between String_A and String_B is:  1

So from the above we can get an idea about how levenshtein distance works, in this example the distance is 1 because there is only one operation is needed.

Step 4 - Some more examples

string_C = "Hello Jc" string_D= "Hello Jack" print(enchant.utils.levenshtein(string_C, string_D))

2

string_E = "My nam i S" string_F = "My name is Sam" print(enchant.utils.levenshtein(string_E, string_F))

4

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