Lemmatization in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the WordNetLemmatizer from nltk.

NLP

from nltk.stem import [1]

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AWordNetLemmatizer

BSnowballStemmer

CPorterStemmer

DLancasterStemmer

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a stemmer class instead of a lemmatizer.

Misspelling the class name.

The WordNetLemmatizer is the class used for lemmatization in nltk.

2fill in blank

medium

Complete the code to create a lemmatizer object.

NLP

lemmatizer = [1]()

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AWordNetLemmatizer

BLemmatizer

CTokenizer

DStemmer

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to instantiate a class that does not exist.

Using stemmer classes instead.

You create a lemmatizer by calling WordNetLemmatizer().

3fill in blank

hard

Fix the error in the code to lemmatize the word 'running'.

NLP

lemma = lemmatizer.[1]('running')

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Atokenize

Bstem

Cparse

Dlemmatize

Attempts:

3 left

💡 Hint

Common Mistakes

Using stem() which is for stemming, not lemmatization.

Using tokenize() which splits text, not lemmatizes.

The method to get the lemma of a word is lemmatize().

4fill in blank

hard

Fill both blanks to lemmatize the word 'better' as an adjective.

NLP

lemma = lemmatizer.[1]('better', pos=[2])

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Alemmatize

B'v'

C'a'

D'n'

Attempts:

3 left

💡 Hint

Common Mistakes

Not specifying the part of speech, so 'better' returns 'better' instead of 'good'.

Using pos='v' (verb) or pos='n' (noun) incorrectly.

Use lemmatize() with pos='a' to lemmatize adjectives like 'better'.

5fill in blank

hard

Fill all three blanks to create a dictionary of words and their lemmas for nouns only.

NLP

words = ['cars', 'geese', 'mice']
lemmas = {word: lemmatizer.[1](word, pos=[2]) for word in words if word.endswith([3])}

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Alemmatize

B'n'

C's'

D'ing'

Attempts:

3 left

💡 Hint

Common Mistakes

Not specifying pos='n' so lemmatization defaults to noun but may be ambiguous.

Filtering with wrong suffix like 'ing' which is for verbs.

Use lemmatize() with pos='n' for nouns. The filter checks words ending with 's' to focus on plural nouns.