Python - 词干提取和词形还原

在自然语言处理领域，我们经常会遇到两个或多个单词具有共同词根的情况。例如，三个单词 - agreed、agreeing 和 agreeable 具有相同的词根 agree。涉及任何这些单词的搜索都应该将它们视为同一个词，即词根。因此，将所有单词链接到它们的词根变得至关重要。NLTK 库具有执行此链接的方法，并输出显示词根的结果。

以下程序使用 Porter 词干提取算法进行词干提取。

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))

当我们执行以上代码时，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提取，但它为单词带来了上下文。因此，它通过将具有相似含义的单词链接到一个单词来更进一步。例如，如果一段话包含 cars、trains 和 automobile 等单词，那么它将把它们全部链接到 automobile。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))

当我们执行以上代码时，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