In this Python NLP lesson we are going to learn about Python NLP Lemmatization, so in Natural Language Processing Lemmatization process is the same as stemming process, but it brings context to the words. so it links words with similar meaning to one word. and unlike stemming, you are always left with a valid word that means the same thing. However, the word you end up with can be completely different.
Now let’s create a simple example on NLP Lemmatization, in this example we want to lemmatize or stem a few words, also you can see that in the second and third example we are using POS or Parts of Speech Tagging.
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from nltk.stem import WordNetLemmatizer lematizer = WordNetLemmatizer() print(lematizer.lemmatize('eating')) print(lematizer.lemmatize('eating', pos='v')) print(lematizer.lemmatize("better", pos='a')) |
This will be the result.
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eating eat good Process finished with exit code 0 |
Now let’s see the difference between stem and lemmatization, we have used the same word with stemming and lemmatization.
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from nltk.stem import WordNetLemmatizer, PorterStemmer lematizer = WordNetLemmatizer() stemmer = PorterStemmer() print(stemmer.stem('believes')) print(lematizer.lemmatize('believes')) |
If you run the code you will see that with the lemmatization we have more accurate result than the stemming, and the lematizer found the valid root for the word.
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believ belief Process finished with exit code 0 |
