The idea of stemming is a sort of normalizing method. Many variations of words carry the same meaning, other than when tense is involved.
The reason why we stem is to shorten the lookup, and normalize sentences.
This sentence means the same thing. in the car is the same. I was is the same. the ing denotes a clear past-tense in both cases, so is it truly necessary to differentiate between ride and riding, in the case of just trying to figure out the meaning of what this past-tense activity was?
No, not really.
This is just one minor example, but imagine every word in the English language, every possible tense and affix you can put on a word. Having individual dictionary entries per version would be highly redundant and inefficient, especially since, once we convert to numbers, the "value" is going to be identical.
One of the most popular stemming algorithms is the Porter stemmer, which has been around since 1979.
First, we're going to grab and define our stemmer:
from nltk.stem import PorterStemmer from nltk.tokenize import sent_tokenize, word_tokenize ps = PorterStemmer()
Now, let's choose some words with a similar stem, like:
example_words = ["python","pythoner","pythoning","pythoned","pythonly"]
Next, we can easily stem by doing something like:
for w in example_words: print(ps.stem(w))
python python python python pythonli
Now let's try stemming a typical sentence, rather than some words:
new_text = "It is important to by very pythonly while you are pythoning with python. All pythoners have pythoned poorly at least once."
words = word_tokenize(new_text) for w in words: print(ps.stem(w))
Now our result is:
It is import to by veri pythonli while you are python with python . All python have python poorli at least onc .
Next up, we're going to discuss something a bit more advanced from the NLTK module, Part of Speech tagging, where we can use the NLTK module to identify the parts of speech for each word in a sentence.