🧠 NLP Mini Assignment – Python

This project contains three simple NLP exercises implemented in Python for learning and demonstration purposes.

📘 Exercises

1. Regex for Email Extraction

Used Python’s re module to extract valid email addresses from text using regular expressions.

Example Output: Extracted emails: ['info@university.edu', 'contact@datasciencehub.org', 'laiba.azhar01@gmail.com']

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2. Compute Edit Distance

Implemented Levenshtein Edit Distance to measure how many edits (insertions, deletions, substitutions) are required to convert one string into another.

Example: Edit distance between 'kitten' and 'sitting': 3 Edit distance between 'flaw' and 'lawn': 2 Edit distance between 'intention' and 'execution': 5 Edit distance between '' and 'abc': 3 Edit distance between 'abc' and 'abc': 0

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3. Normalize and Tokenize Tweets

Performed basic tweet cleaning using regular expressions and NLTK — removed mentions, URLs, emojis, and stopwords.

Example Output: Original: RT @JohnDoe: Loving the new AI model! 😍🔥 Check it out: https://t.co/xyz123 #AI #NLP Clean Tweet: rt loving the new ai model check it out ai nlp Tokens: ['rt', 'loving', 'the', 'new', 'ai', 'model', 'check', 'it', 'out', 'ai', 'nlp']

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🧰 Tools Used

• Python 3.x
• re (Regular Expressions)
• nltk
• string

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✨ Author

Laiba Azhar
MS Data Science | NLP Enthusiast
LinkedIn Profile | GitHub Profile