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>> from nltk.stem import WordNetLemmatizer as lm1
>> from nltk import WordNetLemmatizer as lm2
>> from nltk.stem.wordnet import WordNetLemmatizer as lm3
For me all of the three works the same way, but just to confirm, do they provide anything different?
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Wordnet Lemmatizer It is one of the earliest and most commonly used lemmatizer technique.
Lemmatization in NLTK is the algorithmic process of finding the lemma of a word depending on its meaning and context. Lemmatization usually refers to the morphological analysis of words, which aims to remove inflectional endings. It helps in returning the base or dictionary form of a word known as the lemma.
Short answer- go with stemming when the vocab space is small and the documents are large. Conversely, go with word embeddings when the vocab space is large but the documents are small. However, don't use lemmatization as the increased performance to increased cost ratio is quite low.
No they're not different they're all the same.
from nltk.stem import WordNetLemmatizer as lm1
from nltk import WordNetLemmatizer as lm2
from nltk.stem.wordnet import WordNetLemmatizer as lm3
lm1 == lm2
>>> True
lm2 == lm3
>>> True
lm1 == lm3
>>> True
As corrected by erip why this is happening is because :
That Class(WordNetLemmatizer) is origanlly written in nltk.stem.wordnet so you can do from nltk.stem.wordnet import WordNetLemmatizer as lm3
Which is also import in nltk __init__.py file so you can do from nltk import WordNetLemmatizer as lm2
And is also imported in __init__.py nltk.stem module so you can do from nltk.stem import WordNetLemmatizer as lm1
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