Python - 詞幹提取和詞形還原

在自然語言處理領域，我們經常會遇到兩個或多個單詞具有共同詞根的情況。例如，三個單詞 - agreed、agreeing 和 agreeable 具有相同的詞根 agree。涉及任何這些單詞的搜尋都應該將它們視為同一個詞，即詞根。因此，將所有單詞連結到它們的詞根變得至關重要。NLTK 庫具有執行此連結的方法，並輸出顯示詞根的結果。

以下程式使用 Porter 詞幹提取演算法進行詞幹提取。

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))

當我們執行以上程式碼時，它會產生以下結果。

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

詞形還原類似於詞幹提取，但它為單詞帶來了上下文。因此，它透過將具有相似含義的單詞連結到一個單詞來更進一步。例如，如果一段話包含 cars、trains 和 automobile 等單詞，那麼它將把它們全部連結到 automobile。在下面的程式中，我們使用 WordNet 詞彙資料庫進行詞形還原。

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))

當我們執行以上程式碼時，它會產生以下結果。

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