https://www.pythonprogramming.net/lemmatizing-nltk-tutorial/?completed=/named-entity-recognition-nltk-tutorial/
Lemmatizing with NLTK
A very similar operation to stemming is called lemmatizing. The
major difference between these is, as you saw earlier, stemming can
often create non-existent words, whereas lemmas are actual words.
So, your root stem, meaning the word you end up with, is not
something you can just look up in a dictionary, but you can look up a
lemma.
Some times you will wind up with a very similar word, but sometimes,
you will wind up with a completely different word. Let‘s see some
examples.
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
print(lemmatizer.lemmatize("cats"))
print(lemmatizer.lemmatize("cacti"))
print(lemmatizer.lemmatize("geese"))
print(lemmatizer.lemmatize("rocks"))
print(lemmatizer.lemmatize("python"))
print(lemmatizer.lemmatize("better", pos="a"))
print(lemmatizer.lemmatize("best", pos="a"))
print(lemmatizer.lemmatize("run"))
print(lemmatizer.lemmatize("run",‘v‘))
Here, we‘ve got a bunch of examples of the lemma for the words that we use. The only major thing to note is that lemmatize takes a part of speech parameter, "pos." If not supplied, the default is "noun." This means that an attempt will be made to find the closest noun, which can create trouble for you. Keep this in mind if you use lemmatizing!
In the next tutorial, we‘re going to dive into the NTLK corpus that came with the module, looking at all of the awesome documents they have waiting for us there.