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]

Drag options to blanks, or click blank then click option'

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]()

Drag options to blanks, or click blank then click option'

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')

Drag options to blanks, or click blank then click option'

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])

Drag options to blanks, or click blank then click option'

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])}

Drag options to blanks, or click blank then click option'

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.

Practice

1. What is the main purpose of lemmatization in natural language processing?

easy

A. To find the base or dictionary form of a word

B. To count the frequency of words in a text

C. To translate text from one language to another

D. To remove stop words from a sentence

Solution

1. Step 1: Understand the goal of lemmatization

   Lemmatization simplifies words by converting them to their base or dictionary form, like 'running' to 'run'.

2. Step 2: Compare with other options

   Counting words, translating, or removing stop words are different NLP tasks unrelated to lemmatization.

3. Final Answer:

   To find the base or dictionary form of a word -> Option A

4. Quick Check:

   Lemmatization = base form extraction [OK]

Hint: Lemmatization = find root word form [OK]

Common Mistakes:

• Confusing lemmatization with stemming
• Thinking it counts words
• Mixing it with translation tasks

2. Which of the following is the correct way to use the WordNetLemmatizer from NLTK to lemmatize the word 'better' as an adjective?

easy

A. lemmatizer.lemmatize('better', pos='a')

B. lemmatizer.lemmatize('better', pos='v')

C. lemmatizer.lemmatize('better')

D. lemmatizer.lemmatize('better', pos='n')

Solution

1. Step 1: Identify correct POS tag for adjective

   In NLTK, 'a' is the POS tag for adjective, so to lemmatize 'better' as adjective, use pos='a'.

2. Step 2: Check other POS tags

   'v' is verb, 'n' is noun, and no POS defaults to noun, which is incorrect here.

3. Final Answer:

   lemmatizer.lemmatize('better', pos='a') -> Option A

4. Quick Check:

   POS 'a' = adjective lemmatization [OK]

Hint: Use pos='a' for adjectives in lemmatizer [OK]

Common Mistakes:

• Omitting POS tag defaults to noun
• Using wrong POS like 'v' for adjective
• Confusing POS tags with part of speech names

3. What will be the output of the following Python code using NLTK's WordNetLemmatizer?

from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
print(lemmatizer.lemmatize('wolves'))

medium

A. 'wolves'

B. Error: missing POS argument

C. 'wolve'

D. 'wolf'

Solution

1. Step 1: Understand default POS in lemmatize()

   By default, lemmatize() assumes POS='n' (noun). 'wolves' is plural noun.

2. Step 2: Lemmatize plural noun

   The lemmatizer converts plural nouns to singular, so 'wolves' becomes 'wolf'.

3. Final Answer:

   'wolf' -> Option D

4. Quick Check:

   Plural noun 'wolves' -> singular 'wolf' [OK]

Hint: Default POS='n' converts plurals to singular [OK]

Common Mistakes:

• Expecting output to be unchanged plural
• Thinking POS argument is mandatory
• Confusing lemmatization with stemming

4. Consider this code snippet:

from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
word = 'running'
print(lemmatizer.lemmatize(word))

Why does the output remain 'running' instead of 'run'?

medium

A. Because the lemmatizer cannot process verbs

B. Because the default POS is noun, and 'running' as noun stays unchanged

C. Because the word is misspelled

D. Because lemmatization always returns the original word

Solution

1. Step 1: Check default POS in lemmatize()

   Without specifying POS, lemmatize() treats words as nouns by default.

2. Step 2: Analyze 'running' as noun

   As a noun, 'running' is valid and unchanged, so output remains 'running'.

3. Final Answer:

   Because the default POS is noun, and 'running' as noun stays unchanged -> Option B

4. Quick Check:

   Default POS noun keeps 'running' unchanged [OK]

Hint: Specify POS='v' to lemmatize verbs correctly [OK]

Common Mistakes:

• Assuming lemmatizer always changes words
• Not specifying POS for verbs
• Thinking 'running' is misspelled

5. You want to lemmatize the sentence 'The striped bats are hanging on their feet.' correctly using NLTK. Which approach will give the best lemmatization results?

hard

A. Lemmatize each word without POS tags

B. Remove stop words before lemmatization

C. Lemmatize each word with POS tags obtained from POS tagging

D. Use stemming instead of lemmatization

Solution

1. Step 1: Understand importance of POS tags in lemmatization

   Lemmatization accuracy improves when each word's part of speech is known and used.

2. Step 2: Compare approaches

   Lemmatizing without POS tags may give wrong base forms; stemming changes words roughly; removing stop words doesn't improve lemmatization.

3. Final Answer:

   Lemmatize each word with POS tags obtained from POS tagging -> Option C

4. Quick Check:

   POS tagging + lemmatization = best accuracy [OK]

Hint: Use POS tags for accurate lemmatization [OK]

Common Mistakes:

• Skipping POS tagging before lemmatization
• Confusing stemming with lemmatization
• Thinking stop word removal affects lemmatization