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Lemmatization

While continuing your analysis of user reviews, you noticed that stemming sometimes produces non-standard words like "fli" from "flying", which can reduce interpretability. To address this, you'll now use lemmatization, which returns actual words and helps improve the clarity and accuracy of your analysis.

WordNetLemmatizer has been imported, stop_words has been defined, and the necessary NLTK resources have been downloaded.

Bu egzersiz

Natural Language Processing (NLP) in Python

kursunun bir parçasıdır
Kursu Görüntüle

Egzersiz talimatları

  • Create an instance lemmatizer of the WordNetLemmatizer() class.
  • Use the lemmatizer to lemmatize the lower_tokens.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

clean_tokens = ['flying', 'lot', 'lately', 'flights', 'keep', 'getting', 'delayed', 'honestly', 'traveling', 'work', 'gets', 'exhausting', 'endless', 'delays', 'every', 'travel', 'teaches', 'something', 'new']

# Create lemmatizer
lemmatizer = ____()

# Lemmatize each token
lemmatized_tokens = [____.____(____) for ____ in clean_tokens]

print(lemmatized_tokens)
Kodu Düzenle ve Çalıştır