Limitations of classical methods in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple bag-of-words representation of text.

NLP

from sklearn.feature_extraction.text import CountVectorizer
texts = ['I love AI', 'AI loves me']
vectorizer = CountVectorizer()
X = vectorizer.[1](texts)

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

Afit_transform

Btransform

Cfit

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using only fit or transform separately causes errors or incomplete processing.

The fit_transform method learns the vocabulary and transforms the texts into vectors in one step.

2fill in blank

medium

Complete the code to remove stop words from the text using CountVectorizer.

NLP

vectorizer = CountVectorizer(stop_words=[1])
X = vectorizer.fit_transform(texts)

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

A'english'

BNone

CTrue

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Using True or False does not remove stop words automatically.

Setting stop_words='english' removes common English stop words automatically.

3fill in blank

hard

Fix the error in the code that tries to vectorize text but uses an incorrect method.

NLP

vectorizer = CountVectorizer()
X = vectorizer.[1](['sample text'])

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

Apredict

Bfit_transform

Ctransform

Dfit_predict

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict or fit_predict causes attribute errors.

The CountVectorizer does not have a predict or fit_predict method. Use fit_transform to learn and transform the text.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters words longer than 3 characters.

NLP

filtered_words = {word: len(word) for word in words if len(word) [1] 3 and word [2] 'the'}

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

A>

B<

C!=

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' or '==' changes the filter logic incorrectly.

The code keeps words longer than 3 characters and excludes the word 'the'.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths, excluding short words.

NLP

result = [1]: [2] for w in words if len(w) [3] 4

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

Aw.upper()

Blen(w)

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' includes short words, changing the result.

The comprehension maps uppercase words to their lengths, only for words longer than 4 characters.