Stopword removal in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this stopword removal code?

Given the code below that removes stopwords from a sentence, what is the output list?

NLP

from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

sentence = "This is a simple example to demonstrate stopword removal."
stop_words = set(stopwords.words('english'))
words = word_tokenize(sentence)
filtered_words = [w for w in words if w.lower() not in stop_words]
print(filtered_words)

A ['simple', 'example', 'demonstrate', 'stopword', 'removal', '.']

B ['This', 'is', 'a', 'simple', 'example', 'to', 'demonstrate', 'stopword', 'removal', '.']

C ['simple', 'example', 'demonstrate', 'stopword', 'removal']

D ['This', 'simple', 'example', 'to', 'demonstrate', 'stopword', 'removal', '.']

💡 Hint

Stopwords are common words like 'is', 'a', 'to' that are removed.

The code removes all words in the stopword list ignoring case. Words like 'is', 'a', 'to' are removed, but punctuation '.' remains.

🧠 Conceptual

intermediate

Why do we remove stopwords in text preprocessing?

What is the main reason to remove stopwords from text data before training a machine learning model?

A To reduce noise and improve model focus on meaningful words

B To increase the size of the vocabulary for better learning

C To remove all punctuation and special characters

D To make the text longer and more detailed

💡 Hint

Think about common words that add little meaning.

Stopwords are common words that usually do not add useful information. Removing them helps the model focus on important words and reduces noise.

❓ Metrics

advanced

How does stopword removal affect model accuracy?

Which statement best describes the typical effect of stopword removal on text classification model accuracy?

A It has no effect on accuracy since stopwords are ignored by models

B It always decreases accuracy because some stopwords carry important meaning

C It usually improves accuracy by reducing noise but may sometimes remove useful context

D It always increases accuracy by making text shorter

💡 Hint

Consider both benefits and risks of removing stopwords.

Stopword removal often improves accuracy by reducing noise, but in some cases, stopwords can carry context, so removal might hurt performance.

🔧 Debug

advanced

Why does this stopword removal code raise an error?

What error does the following code raise and why? from nltk.corpus import stopwords sentence = "Remove stopwords from this sentence." stop_words = stopwords.words('english') filtered = [w for w in sentence.split() if w not in stop_words] print(filtered)

NLP

from nltk.corpus import stopwords
sentence = "Remove stopwords from this sentence."
stop_words = stopwords.words('english')
filtered = [w for w in sentence.split() if w not in stop_words]
print(filtered)

A No error; output is ['Remove', 'stopwords', 'sentence.']

B Raises TypeError because stop_words is not a set

C Raises SyntaxError due to missing colon in list comprehension

D Raises LookupError because stopwords corpus is not downloaded

💡 Hint

Check if nltk data is downloaded before use.

If nltk stopwords corpus is not downloaded, accessing stopwords.words('english') raises a LookupError.

❓ Model Choice

expert

Which model benefits most from stopword removal?

Among these models, which one typically benefits the most from removing stopwords during text preprocessing?

A Transformer-based model like BERT with attention mechanisms

B Bag-of-Words model using term frequency vectors

C Recurrent Neural Network (RNN) with word embeddings

D Convolutional Neural Network (CNN) on raw text characters

💡 Hint

Consider how each model handles common words internally.

Bag-of-Words models rely on word counts and benefit from stopword removal to reduce noise. Transformer models and RNNs can learn to ignore stopwords via context and attention.