What Is The Best Stemming Method In Python?

January 30, 2024 Post a Comment

I tried all the nltk methods for stemming but it gives me weird results with some words. Examples It often cut end of words when it shouldn't do it : poodle => poodl article a

Solution 1:

The results you are getting are (generally) expected for a stemmer in English. You say you tried "all the nltk methods" but when I try your examples, that doesn't seem to be the case.

Here are some examples using the PorterStemmer

importnltkps= nltk.stemmer.PorterStemmer()
ps.stem('grows')
'grow'
ps.stem('leaves')
'leav'
ps.stem('fairly')
'fairli'

The results are 'grow', 'leav' and 'fairli' which, even if they are what you wanted, are stemmed versions of the original word.

If we switch to the Snowball stemmer, we have to provide the language as a parameter.

importnltksno= nltk.stem.SnowballStemmer('english')
sno.stem('grows')
'grow'
sno.stem('leaves')
'leav'
sno.stem('fairly')
'fair'

The results are as before for 'grows' and 'leaves' but 'fairly' is stemmed to 'fair'

So in both cases (and there are more than two stemmers available in nltk), words that you say are not stemmed, in fact, are. The LancasterStemmer will return 'easy' when provided with 'easily' or 'easy' as input.

Maybe you really wanted a lemmatizer? That would return 'article' and 'poodle' unchanged.

importnltklemma= nltk.wordnet.WordNetLemmatizer()
lemma.lemmatize('article')
'article'
lemma.lemmatize('leaves')
'leaf'

Solution 2:

All these stemmers that have been discussed here are algorithmic stemmer,hence they can always produce unexpected results such as

In [3]: from nltk.stem.porter import *

In [4]: stemmer = PorterStemmer()

In [5]: stemmer.stem('identified')
Out[5]: u'identifi'

In [6]: stemmer.stem('nonsensical')
Out[6]: u'nonsens'

To correctly get the root words one need a dictionary based stemmer such as Hunspell Stemmer.Here is a python implementation of it in the following link. Example code is here

>>> import hunspell
>>> hobj = hunspell.HunSpell('/usr/share/myspell/en_US.dic', '/usr/share/myspell/en_US.aff')
>>> hobj.spell('spookie')
False>>> hobj.suggest('spookie')
['spookier', 'spookiness', 'spooky', 'spook', 'spoonbill']
>>> hobj.spell('spooky')
True>>> hobj.analyze('linked')
[' st:link fl:D']
>>> hobj.stem('linked')
['link']

Solution 3:

Stemmers vary in their aggressiveness. Porter is one of the monst aggressive stemmer for English. I find it usually hurts more than it helps. On the lighter side you can either use a lemmatizer instead as already suggested, or a lighter algorithmic stemmer. The limitation of lemmatizers is that they cannot handle unknown words.

Personally I like the Krovetz stemmer which is a hybrid solution, combing a dictionary lemmatizer and a light weight stemmer for out of vocabulary words. Krovetz also kstem or light_stemmer option in Elasticsearch. There is a python implementation on pypi https://pypi.org/project/KrovetzStemmer/, though that is not the one that I have used.

Another option is the the lemmatizer in spaCy. Afte processing with spaCy every token has a lemma_ attribute. (note the underscore lemma hold a numerical identifier of the lemma_) - https://spacy.io/api/token

Here are some papers comparing various stemming algorithms:

Solution 4:

Stemming is all about removing suffixes(usually only suffixes, as far as I have tried none of the nltk stemmers could remove a prefix, forget about infixes). So we can clearly call stemming as a dumb/ not so intelligent program. It doesn't check if a word has a meaning before or after stemming. For eg. If u try to stem "xqaing", although not a word, it will remove "-ing" and give u "xqa".

So, in order to use a smarter system, one can use lemmatizers. Lemmatizers uses well-formed lemmas (words) in form of wordnet and dictionaries. So it always returns and takes a proper word. However, it is slow because it goes through all words in order to find the relevant one.

Solution 5:

In my chatbot project I have used PorterStemmer However LancasterStemmer also serves the purpose. Ultimate objective is to stem the word to its root so that we can search and compare with the search words inputs.

For Example: from nltk.stem import PorterStemmer ps = PorterStemmer()

defSrchpattrnStmmed(self):
    KeyWords =[]
    SrchpattrnTkn = word_tokenize(self.input)
    for token in SrchpattrnTkn:
        if token notin stop_words:
            KeyWords.append(ps.stem(token))
            continue#print(KeyWords)return KeyWords

Hope this will help..

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