Lemmatisierung

Während du deine Analyse von Nutzerbewertungen fortsetzt, ist dir aufgefallen, dass Stemming manchmal nicht standardisierte Wörter wie „fli“ aus „flying“ erzeugt, was die Interpretierbarkeit verringern kann. Um dem entgegenzuwirken, verwendest du jetzt Lemmatisierung, die echte Wörter zurückgibt und die Klarheit und Genauigkeit deiner Analyse verbessert.

WordNetLemmatizer wurde importiert, stop_words ist definiert, und die erforderlichen NLTK-Ressourcen wurden heruntergeladen.

Diese Übung ist Teil des Kurses

<Kurs>Natural Language Processing (NLP) in Python</Kurs>

Übungsanweisungen

Erstelle eine Instanz lemmatizer der Klasse WordNetLemmatizer().
Verwende den lemmatizer, um die lower_tokens zu lemmatisieren.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

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)

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

<Kurs>Natural Language Processing (NLP) in Python</Kurs>

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4.9+

Kurs kostenlos starten

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: Einführung in die Verarbeitung natürlicher Sprache Exercise 2: Satz- und Wort-Tokenisierung Exercise 3: NLP-Workflow Exercise 4: Umgang mit Stoppwörtern und Satzzeichen Exercise 5: Stoppwörter entfernen Exercise 6: Satzzeichen entfernen Exercise 7: Techniken der Textnormalisierung Exercise 8: Kleinschreibung Exercise 9: Stemming Exercise 10: Lemmatisierung

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