Punctuation and special character removal in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to remove punctuation from the text using str.translate.

NLP

import string
text = "Hello, world!"
clean_text = text.[1](str.maketrans('', '', string.punctuation))
print(clean_text)

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

Astrip

Breplace

Ctranslate

Dsplit

Attempts:

3 left

💡 Hint

Common Mistakes

Using replace without a loop removes only one character at a time.

Using strip only removes characters from the start and end of the string.

The translate method with str.maketrans removes all punctuation characters from the string.

2fill in blank

medium

Complete the code to remove all characters that are not letters or spaces using a list comprehension.

NLP

text = "Hello, world! 123"
clean_text = ''.join([c for c in text if [1] or c == ' '])
print(clean_text)

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

Ac.isalpha()

Bc.isdigit()

Cc.isupper()

Dc.isspace()

Attempts:

3 left

💡 Hint

Common Mistakes

Using isdigit() keeps numbers instead of letters.

Using isspace() only keeps spaces, removing letters.

The isalpha() method checks if a character is a letter. We keep letters and spaces only.

3fill in blank

hard

Fix the error in the code to remove punctuation using regex.

NLP

import re
text = "Hello, world!"
clean_text = re.sub([1], '', text)
print(clean_text)

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

A'[a-zA-Z]'

B'[!?,.]'

C'[0-9]'

D'[^a-zA-Z ]'

Attempts:

3 left

💡 Hint

Common Mistakes

Using '[a-zA-Z]' removes letters instead of punctuation.

Using '[0-9]' removes digits only, not punctuation.

The regex '[^a-zA-Z ]' matches any character that is NOT a letter or space, so it removes punctuation and digits.

4fill in blank

hard

Fill both blanks to create a function that removes punctuation and converts text to lowercase.

NLP

import string

def clean_text(text):
    return text.[1](str.maketrans('', '', string.[2])).lower()

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

Atranslate

Breplace

Cpunctuation

Dwhitespace

Attempts:

3 left

💡 Hint

Common Mistakes

Using replace instead of translate does not remove all punctuation at once.

Using string.whitespace removes spaces, which we want to keep.

The translate method removes all punctuation characters defined in string.punctuation. Then lower() converts text to lowercase.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps words to their cleaned versions without punctuation and in lowercase.

NLP

import string
words = ['Hello!', 'World?', 'Test.']
clean_words = {word[1]: word.[2](str.maketrans('', '', string.[3])).lower() for word in words}
print(clean_words)

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

A.lower()

Btranslate

Cpunctuation

D.strip()

Attempts:

3 left

💡 Hint

Common Mistakes

Using strip removes only whitespace, not punctuation.

Not converting to lowercase causes inconsistent keys.

The dictionary comprehension uses translate with string.punctuation to remove punctuation, then lower() to convert to lowercase.

Practice

1. What is the main purpose of removing punctuation and special characters in text preprocessing for NLP?

easy

A. To increase the length of the text

B. To clean text for better machine understanding

C. To add more special symbols for emphasis

D. To make the text harder to read

Solution

1. Step 1: Understand text preprocessing goals

   Text preprocessing aims to simplify text so machines can analyze it better.

2. Step 2: Role of punctuation removal

   Removing punctuation and special characters reduces noise and irrelevant symbols in text.

3. Final Answer:

   To clean text for better machine understanding -> Option B

4. Quick Check:

   Text cleaning = Better machine understanding [OK]

Hint: Removing punctuation cleans text for easier analysis [OK]

Common Mistakes:

• Thinking punctuation adds meaning for machines
• Believing removal increases text length
• Assuming special characters improve model accuracy

2. Which Python code snippet correctly removes punctuation from the string text = "Hello, world!" using regular expressions?

easy

A. re.sub(r'[\w]', '', text)

B. re.sub(r'[\d]', '', text)

C. re.sub(r'[\W]', '', text)

D. re.sub(r'[\s]', '', text)

Solution

1. Step 1: Understand regex classes

   \W matches any non-word character, including punctuation.

2. Step 2: Apply regex to remove punctuation

   Using re.sub(r'[\W]', '', text) removes punctuation and special characters.

3. Final Answer:

   re.sub(r'[\W]', '', text) -> Option C

4. Quick Check:

   \W removes punctuation [OK]

Hint: Use \W in regex to remove punctuation [OK]

Common Mistakes:

• Using \w which matches word characters, not punctuation
• Using \d which matches digits only
• Using \s which matches spaces, not punctuation

3. What will be the output of this Python code?

import re
text = "Hello, world! Let's clean: this text."
clean_text = re.sub(r'[^\\w\\s]', '', text)
print(clean_text)

medium

A. Hello world Lets clean this text

B. Hello, world! Let's clean: this text.

C. Hello world! Let's clean this text.

D. Hello world Lets clean this text.

Solution

1. Step 1: Understand regex pattern

   Pattern '[^\w\s]' matches any character that is NOT a word character or whitespace, i.e., punctuation.

2. Step 2: Apply substitution

   All punctuation marks like commas, apostrophes, colons, and periods are removed.

3. Final Answer:

   Hello world Lets clean this text -> Option A

4. Quick Check:

   Removed punctuation, kept words and spaces [OK]

Hint: Regex [^\w\s] removes punctuation, keeps words and spaces [OK]

Common Mistakes:

• Expecting apostrophes to remain
• Confusing \w with punctuation
• Not noticing spaces are preserved

4. Identify the error in this code snippet intended to remove punctuation:

import re
text = "Good morning! How are you?"
clean_text = re.sub(r'[\w]', '', text)
print(clean_text)

medium

A. The print statement syntax is incorrect

B. The code is missing import statement

C. The regex pattern is correct for punctuation removal

D. The regex removes word characters instead of punctuation

Solution

1. Step 1: Analyze regex pattern

   Pattern '[\w]' matches word characters (letters, digits), not punctuation.

2. Step 2: Effect on text

   It removes letters, leaving punctuation and spaces, opposite of intended.

3. Final Answer:

   The regex removes word characters instead of punctuation -> Option D

4. Quick Check:

   Wrong regex removes words, not punctuation [OK]

Hint: Use \W to remove punctuation, not \w [OK]

Common Mistakes:

• Confusing \w and \W in regex
• Assuming code lacks imports
• Thinking print syntax is wrong

5. You have a dataset with text containing emojis and punctuation. You want to remove only punctuation but keep emojis. Which approach is best?

hard

A. Use regex to remove only ASCII punctuation characters

B. Use regex to remove all non-word and non-space characters

C. Remove all characters except letters and digits

D. Replace emojis with empty string and keep punctuation

Solution

1. Step 1: Understand emoji vs punctuation

   Emojis are special Unicode symbols, not ASCII punctuation.

2. Step 2: Choose selective removal

   Removing only ASCII punctuation preserves emojis, unlike broad regex removing all non-word chars.

3. Final Answer:

   Use regex to remove only ASCII punctuation characters -> Option A

4. Quick Check:

   Selective ASCII punctuation removal keeps emojis [OK]

Hint: Remove ASCII punctuation only to keep emojis [OK]

Common Mistakes:

• Removing all non-word chars removes emojis too
• Removing all except letters/digits loses emojis
• Replacing emojis instead of punctuation