📜 Natural Language Processing (NLP) Basics

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📜 Natural Language Processing (NLP) Basics

Learn how machines understand human languages! 🌍💬

Natural Language Processing (NLP) is a branch of AI that focuses on helping machines read, interpret, and generate human languages. It powers applications like chatbots 🤖, translators 🌐, and voice assistants 🎤. Let's dive deeper into its core concepts, techniques, and real-world use cases! 🚀
Concept Meaning Example
Tokenization 🪙 Splitting text into individual words or phrases. "I love AI" → ["I", "love", "AI"]
Stemming & Lemmatization 🌱 Reducing words to their root form. "Running" ➔ "run"
Stopword Removal 🚫 Eliminating common words that add little meaning. Removing "is", "the", "and", etc.
Text Vectorization 📊 Converting words into numerical form. Using Bag of Words, TF-IDF, Word2Vec

🪙 Tokenization

- Tokenization breaks down large bodies of text into smaller units like words, sentences, or phrases.
- Important for text analysis and further NLP tasks.

Example:
🔹 Sentence: "ChatGPT is awesome!" ➔ Tokens: ["ChatGPT", "is", "awesome", "!"]

🌱 Stemming & Lemmatization

- Stemming: Chops words to base forms crudely ("running" ➔ "run").
- Lemmatization: Finds proper dictionary root forms ("better" ➔ "good").

Why? 🔹 Reduces vocabulary size.
🔹 Makes machine learning models more efficient.

🚫 Stopword Removal

- Stopwords like "the", "is", "and" are common but carry less importance.
- Removing them focuses the model on meaningful words.

Example:
🔹 "The sky is blue" ➔ "sky blue"

📊 Text Vectorization

- Text data must be converted to numbers to work with ML models.
- Methods:
🔹 Bag of Words: Counts word occurrences.
🔹 TF-IDF: Highlights important words.
🔹 Word Embeddings: Captures semantic meaning.

Example: 🔹 "Apple is red" ➔ [1, 0, 1] (word counts)

🎯 Quick Quiz!

Which method helps in reducing words to their dictionary root form?

🛠️ Try This!

Given the sentence: "Data Science is transforming the world", can you:

  • ✅ Tokenize it
  • ✅ Remove stopwords
  • ✅ Stem the words

(Write your answer in a notebook!)

By Darchums Technologies Inc - April 28, 2025

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