Working process of stemming and lemmatization

If we pass text data in our machine learning or deep learning models then these models will not understand those text data. So what we do is that, we try to pre-process the data and try to convert it into a numerical representation. We call these numerical representation as vectors.

What is Stemming?

Stemming is the process where remove prefixes and suffixes from words, so that they are reduced to a simpler form which called stems.

For example:
If we have words like history or historical and after stemming the words will convert into histori. If we have words like finally or final or finalized then after stemming the words will convert into fina. Similarly, go or goes will convert into go.

So here we can say that we are removing suffix and prefix and convert in into simple form.

What is Lemmatization?

Lemmatization is the process where we mapped several different forms of same words to one single form and it is called the root form. This root form is called as lemma.

For example:
If we have words like history or historical and after stemming it will convert into history. If we have words like finally or final or finalized then after stemming it will convert into final.
The words we get after doing stemming are not meaningful. The words we get after stemming we can't say the meaning of that converter word. In Lemmatization the target is same as stemming but in lemmatization, we get more meaningful words than stemming. It means after performing lemmatization, we can say the meaning of the converted word.

Difference between stemming and lemmatization:

1. In Lemmatization we always get a meaningful word but in stemming sometimes we get meaningful words and sometimes not.
2. Lemmatization takes more time than stemming.