Punctuation and special character removal in NLP - ML Experiment: Train & Evaluate

Experiment - Punctuation and special character removal

Problem:You have text data with many punctuation marks and special characters. These can confuse your model when learning from text. The current preprocessing keeps all punctuation and special characters.

Current Metrics:Text samples still contain punctuation like commas, periods, and symbols like #, @. This causes noisy input for models.

Issue:The text data is noisy due to punctuation and special characters, which can reduce model accuracy in NLP tasks.

Your Task

Remove all punctuation and special characters from the text data to clean it. The cleaned text should only contain letters, numbers, and spaces.

Do not remove letters or numbers.

Do not remove spaces between words.

Use Python standard libraries only.

Hint 1

Hint 2

Hint 3

Solution

NLP

import re
import string

def clean_text(text):
    # Create a pattern to match any character that is NOT a letter, number, or space
    pattern = r"[^a-zA-Z0-9 ]+"
    # Replace matched characters with empty string
    cleaned = re.sub(pattern, '', text)
    return cleaned

# Example usage
sample_text = "Hello, world! This is a test: #NLP @2024."
cleaned_text = clean_text(sample_text)
print(f"Original: {sample_text}")
print(f"Cleaned: {cleaned_text}")

Added a function to remove punctuation and special characters using regex.

Kept letters, numbers, and spaces intact.

Tested the function on a sample sentence with punctuation and special characters.

Results Interpretation

Before cleaning: "Hello, world! This is a test: #NLP @2024."

After cleaning: "Hello world This is a test NLP 2024"

Removing punctuation and special characters cleans the text data, making it easier for NLP models to learn meaningful patterns without noise.

Bonus Experiment

Try removing punctuation but keep special characters like @ and # for social media text analysis.

💡 Hint

Modify the regex pattern to exclude @ and # from removal.