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3. Python Stemming and Lemmatization

Python Stemming and Lemmatization

在自然语言处理领域，我们遇到两个或多个单词具有共同根的情况。 例如，三个词 - 同意，同意和同意具有相同的词根同意。 涉及任何这些词的搜索应将它们视为同一个词，即根词。 因此，将所有单词链接到根词中变得至关重要。 NLTK库具有执行此链接的方法，并提供显示根词的输出。

以下程序使用Porter Stemming算法进行词干分析。

import nltk
from nltk.stem.porter import PorterStemmer
porter_stemmer = PorterStemmer()
word_data = "It originated from the idea that there are readers who prefer learning new skills from the comforts of their drawing rooms"
# First Word tokenization
nltk_tokens = nltk.word_tokenize(word_data)
#Next find the roots of the word
for w in nltk_tokens:
       print "Actual: %s  Stem: %s"  % (w,porter_stemmer.stem(w))

当我们执行上面的代码时，它会产生以下结果。

Actual: It  Stem: It
Actual: originated  Stem: origin
Actual: from  Stem: from
Actual: the  Stem: the
Actual: idea  Stem: idea
Actual: that  Stem: that
Actual: there  Stem: there
Actual: are  Stem: are
Actual: readers  Stem: reader
Actual: who  Stem: who
Actual: prefer  Stem: prefer
Actual: learning  Stem: learn
Actual: new  Stem: new
Actual: skills  Stem: skill
Actual: from  Stem: from
Actual: the  Stem: the
Actual: comforts  Stem: comfort
Actual: of  Stem: of
Actual: their  Stem: their
Actual: drawing  Stem: draw
Actual: rooms  Stem: room

词形还原类似于词干，但它为词语带来了语境。因此，通过将具有相似含义的词语与一个词语联系起来，它更进了一步。 例如，如果一个段落有像汽车，火车和汽车这样的词，那么它将把它们全部链接到汽车上。 在下面的程序中，我们使用WordNet词汇数据库进行词形还原。

import nltk
from nltk.stem import WordNetLemmatizer
wordnet_lemmatizer = WordNetLemmatizer()
word_data = "It originated from the idea that there are readers who prefer learning new skills from the comforts of their drawing rooms"
nltk_tokens = nltk.word_tokenize(word_data)
for w in nltk_tokens:
       print "Actual: %s  Lemma: %s"  % (w,wordnet_lemmatizer.lemmatize(w))

当我们执行上面的代码时，它会产生以下结果。

Actual: It  Lemma: It
Actual: originated  Lemma: originated
Actual: from  Lemma: from
Actual: the  Lemma: the
Actual: idea  Lemma: idea
Actual: that  Lemma: that
Actual: there  Lemma: there
Actual: are  Lemma: are
Actual: readers  Lemma: reader
Actual: who  Lemma: who
Actual: prefer  Lemma: prefer
Actual: learning  Lemma: learning
Actual: new  Lemma: new
Actual: skills  Lemma: skill
Actual: from  Lemma: from
Actual: the  Lemma: the
Actual: comforts  Lemma: comfort
Actual: of  Lemma: of
Actual: their  Lemma: their
Actual: drawing  Lemma: drawing
Actual: rooms  Lemma: room