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.

Practice

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

easy

A. To correct spelling mistakes in text

B. To translate text into another language

C. To count the number of words in a sentence

D. To remove common words that do not add much meaning

Solution

1. Step 1: Understand what stopwords are

   Stopwords are common words like 'the', 'is', 'and' that usually don't add important meaning.

2. Step 2: Identify the purpose of removing stopwords

   Removing these words helps focus on meaningful words for better analysis.

3. Final Answer:

   To remove common words that do not add much meaning -> Option D

4. Quick Check:

   Stopword removal = Remove common meaningless words [OK]

Hint: Stopwords are common filler words removed to focus on meaning [OK]

Common Mistakes:

• Thinking stopword removal translates text
• Confusing stopword removal with spell checking
• Believing it counts words instead of removing them

2. Which of the following Python code snippets correctly removes stopwords from a list of words using NLTK?

easy

A. filtered_words = [w for w in words if w not in stopwords.words('english')]

B. filtered_words = [w for w in words if w in stopwords.words('english')]

C. filtered_words = stopwords.remove(words)

D. filtered_words = words.remove(stopwords.words('english'))

Solution

1. Step 1: Understand NLTK stopword removal syntax

   We keep words that are NOT in the stopwords list using a list comprehension.

2. Step 2: Check each option

   filtered_words = [w for w in words if w not in stopwords.words('english')] correctly filters out stopwords. filtered_words = [w for w in words if w in stopwords.words('english')] keeps only stopwords, which is wrong. Options C and D use invalid methods.

3. Final Answer:

   filtered_words = [w for w in words if w not in stopwords.words('english')] -> Option A

4. Quick Check:

   Keep words not in stopwords list = filtered_words = [w for w in words if w not in stopwords.words('english')] [OK]

Hint: Filter words not in stopwords list using list comprehension [OK]

Common Mistakes:

• Using 'in' instead of 'not in' to filter stopwords
• Calling non-existent methods like stopwords.remove()
• Confusing filtering logic to keep stopwords instead of removing

3. Given the code below, what is the output?

import nltk
from nltk.corpus import stopwords
nltk.download('stopwords')
words = ['this', 'is', 'a', 'test']
filtered = [w for w in words if w not in stopwords.words('english')]
print(filtered)

medium

A. ['this', 'test']

B. ['this', 'is', 'a', 'test']

C. ['test']

D. []

Solution

1. Step 1: Identify stopwords in the list

   Stopwords in English include 'this', 'is', 'a'. 'test' is not a stopword.

2. Step 2: Filter out stopwords

   The list comprehension removes 'this', 'is', 'a', leaving only 'test'.

3. Final Answer:

   ['test'] -> Option C

4. Quick Check:

   Only non-stopword 'test' remains [OK]

Hint: Remove common words; only meaningful words remain [OK]

Common Mistakes:

• Assuming all words remain after removal
• Forgetting to download stopwords corpus
• Confusing which words are stopwords

4. The following code is intended to remove stopwords from a list of words, but it raises an error. What is the problem?

from nltk.corpus import stopwords
words = ['hello', 'world']
filtered = [w for w in words if w not in stopwords('english')]
print(filtered)

medium

A. stopwords is not a function; should use stopwords.words('english')

B. The list comprehension syntax is incorrect

C. The variable 'words' is not defined

D. The print statement is missing parentheses

Solution

1. Step 1: Check how stopwords are accessed

   stopwords is a module, and stopwords.words('english') returns the list of stopwords.

2. Step 2: Identify the error in code

   The code calls stopwords('english'), which is invalid and causes an error.

3. Final Answer:

   stopwords is not a function; should use stopwords.words('english') -> Option A

4. Quick Check:

   Use stopwords.words('english') to get stopwords list [OK]

Hint: Use stopwords.words('english'), not stopwords('english') [OK]

Common Mistakes:

• Calling stopwords as a function instead of accessing .words()
• Misunderstanding list comprehension syntax
• Assuming print needs no parentheses in Python 3

5. You want to remove stopwords from a text but keep the word 'not' because it changes meaning. How can you modify the stopword list in NLTK to do this?

hard

A. Add 'not' to the stopwords list before filtering

B. Remove 'not' from the stopwords list before filtering

C. Replace 'not' with a synonym before filtering

D. Ignore stopword removal and keep all words

Solution

1. Step 1: Understand default stopwords list

   NLTK's stopwords list includes 'not', which would be removed by default.

2. Step 2: Modify stopwords list to keep 'not'

   Remove 'not' from the stopwords list before filtering to keep it in the text.

3. Final Answer:

   Remove 'not' from the stopwords list before filtering -> Option B

4. Quick Check:

   Modify stopwords list to keep important words [OK]

Hint: Delete 'not' from stopwords list to keep it in text [OK]

Common Mistakes:

• Adding 'not' to stopwords instead of removing
• Replacing words instead of modifying stopwords
• Skipping stopword removal entirely