Python - Algorithmes de stemming

Dans les domaines du traitement du langage naturel, nous rencontrons des situations dans lesquelles deux mots ou plus ont une racine commune. Par exemple, les trois mots - d'accord, d'accord et d'accord ont le même mot racine d'accord. Une recherche impliquant l'un de ces mots doit les traiter comme le même mot qui est le mot racine. Ainsi, il devient essentiel de lier tous les mots à leur mot racine. La bibliothèque NLTK a des méthodes pour faire cette liaison et donner la sortie montrant le mot racine.

Il existe trois algorithmes de dérivation les plus utilisés disponibles dans nltk. Ils donnent un résultat légèrement différent. L'exemple ci-dessous montre l'utilisation des trois algorithmes de dérivation et leur résultat.

import nltk
from nltk.stem.porter import PorterStemmer
from nltk.stem.lancaster import LancasterStemmer
from nltk.stem import SnowballStemmer 
porter_stemmer = PorterStemmer()
lanca_stemmer = LancasterStemmer()
sb_stemmer = SnowballStemmer("english",)
word_data = "Aging head of famous crime family decides to transfer his position to one of his subalterns" 
# First Word tokenization
nltk_tokens = nltk.word_tokenize(word_data)
#Next find the roots of the word
print '***PorterStemmer****\n'
for w_port in nltk_tokens:
   print "Actual: %s  || Stem: %s"  % (w_port,porter_stemmer.stem(w_port))
print '\n***LancasterStemmer****\n'    
for w_lanca in nltk_tokens:
      print "Actual: %s  || Stem: %s"  % (w_lanca,lanca_stemmer.stem(w_lanca))
print '\n***SnowballStemmer****\n' 
for w_snow in nltk_tokens:
      print "Actual: %s  || Stem: %s"  % (w_snow,sb_stemmer.stem(w_snow))

Lorsque nous exécutons le programme ci-dessus, nous obtenons la sortie suivante -

***PorterStemmer****
Actual: Aging  || Stem: age
Actual: head  || Stem: head
Actual: of  || Stem: of
Actual: famous  || Stem: famou
Actual: crime  || Stem: crime
Actual: family  || Stem: famili
Actual: decides  || Stem: decid
Actual: to  || Stem: to
Actual: transfer  || Stem: transfer
Actual: his  || Stem: hi
Actual: position  || Stem: posit
Actual: to  || Stem: to
Actual: one  || Stem: one
Actual: of  || Stem: of
Actual: his  || Stem: hi
Actual: subalterns  || Stem: subaltern
***LancasterStemmer****
Actual: Aging  || Stem: ag
Actual: head  || Stem: head
Actual: of  || Stem: of
Actual: famous  || Stem: fam
Actual: crime  || Stem: crim
Actual: family  || Stem: famy
Actual: decides  || Stem: decid
Actual: to  || Stem: to
Actual: transfer  || Stem: transf
Actual: his  || Stem: his
Actual: position  || Stem: posit
Actual: to  || Stem: to
Actual: one  || Stem: on
Actual: of  || Stem: of
Actual: his  || Stem: his
Actual: subalterns  || Stem: subaltern
***SnowballStemmer****
Actual: Aging  || Stem: age
Actual: head  || Stem: head
Actual: of  || Stem: of
Actual: famous  || Stem: famous
Actual: crime  || Stem: crime
Actual: family  || Stem: famili
Actual: decides  || Stem: decid
Actual: to  || Stem: to
Actual: transfer  || Stem: transfer
Actual: his  || Stem: his
Actual: position  || Stem: posit
Actual: to  || Stem: to
Actual: one  || Stem: one
Actual: of  || Stem: of
Actual: his  || Stem: his
Actual: subalterns  || Stem: subaltern