Lemmatizasyon

Kullanıcı yorumlarını analiz etmeye devam ederken, stemleme işleminin bazen "flying" kelimesinden "fli" gibi standart olmayan sözcükler ürettiğini ve bunun yorumlanabilirliği azaltabildiğini fark ettin. Bunu çözmek için artık, gerçek sözcükler döndüren ve analizinin açıklığını ve doğruluğunu artırmaya yardımcı olan lemmatizasyonu kullanacaksın.

WordNetLemmatizer içe aktarıldı, stop_words tanımlandı ve gerekli NLTK kaynakları indirildi.

Bu egzersiz

Python ile Natural Language Processing (NLP)

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

WordNetLemmatizer() sınıfının bir örneğini lemmatizer adıyla oluştur.
lemmatizerı kullanarak lower_tokens üzerinde lemmatizasyon yap.

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

Bu egzersiz

Python ile Natural Language Processing (NLP)

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

Learn the essentials of text processing in Natural Language Processing (NLP). Master techniques such as tokenization, stop word and punctuation removal, and text normalization with lowercasing, stemming, and lemmatization to prepare text data for further analysis and insight extraction.

Exercise 1: Doğal dil işlemenin temellerine giriş Exercise 2: Cümle ve kelime tokenizasyonu Exercise 3: NLP iş akışı Exercise 4: Stop words ve noktalama işaretlerini işleme Exercise 5: Durdurma sözcüklerini kaldırma Exercise 6: Noktalama işaretlerini kaldırma Exercise 7: Metin normalleştirme teknikleri Exercise 8: Küçük harfe çevirme Exercise 9: Stemming Exercise 10: Lemmatizasyon

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