🌐 Natural Language Processing (NLP)

Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that enables computers to understand, interpret, and generate human language.

🧠 How NLP Works: Step-by-Step

1. Text Input: User or system provides input as text or speech (converted using speech-to-text).
2. Text Preprocessing:
   • Tokenization: Splits text into words or phrases.
   • Normalization: Converts to lowercase, removes punctuation, etc.
   • Stopword Removal: Removes common words like "the", "is", etc.
   • Stemming/Lemmatization: Reduces words to their base form (e.g., “running” → “run”).
3. Syntactic Analysis (Structure):
   • Part-of-Speech Tagging: Labels each word as noun, verb, etc.
   • Parsing: Analyzes grammatical structure.
4. Semantic Analysis (Meaning):
   • Named Entity Recognition (NER): Identifies people, places, dates.
   • Word Sense Disambiguation: Determines meaning of a word in context.
   • Dependency Parsing: Understands relationships between words.
5. Contextual Understanding: Uses deep learning (BERT, GPT, etc.) to infer context.
6. Task Execution: Examples:
   • Sentiment Classification
   • Translation
   • Question Answering
   • Summarization
   • Text Generation
7. Output Generation: Delivers human-like responses or insights.

🛠️ Key Technologies Used in NLP

• Machine Learning / Deep Learning
• Transformers (e.g., BERT, GPT)
• Large Language Models (LLMs)
• Neural Networks
• Pretrained Embeddings (e.g., Word2Vec, GloVe)

⚙️ NLP Example: Input → Output

Input: “What’s the weather like in Mumbai today?”

NLP Process:

• Tokenize and parse the sentence.
• Identify "weather" as topic, "Mumbai" as location.
• Understand user intent.
• Fetch and deliver weather data.

Output: “It’s mostly sunny in Mumbai today with a high of 32°C.”

🔍 Live NLP Example: Step-by-Step

🎯 Sample Input Sentence

"Apple is launching a new iPhone next month in California."

🔹 Step 1: Text Preprocessing

• Lowercased: "apple is launching a new iphone next month in california."
• Tokenized: ["apple", "is", "launching", "a", "new", "iphone", "next", "month", "in", "california", "."]

🔹 Step 2: Part-of-Speech Tagging

                    apple (NNP)     → Proper Noun
                    is (VBZ)        → Verb
                    launching (VBG) → Verb (Gerund)
                    a (DT)          → Determiner
                    new (JJ)        → Adjective
                    iphone (NN)     → Noun
                    next (JJ)       → Adjective
                    month (NN)      → Noun
                    in (IN)         → Preposition
                    california (NNP)→ Proper Noun
                    . (.)           → Punctuation
                

🔹 Step 3: Named Entity Recognition (NER)

• Apple → ORGANIZATION
• iPhone → PRODUCT (custom NER)
• next month → DATE
• California → LOCATION

🔹 Step 4: Dependency Parsing

• Subject: Apple
• Verb Phrase: is launching
• Object: a new iPhone
• Modifiers: next month, in California

🔹 Step 5: Semantic Understanding

Language models infer Apple is planning a product launch of an iPhone in California next month.

🗣️ Step 6: Task Execution

"Apple plans to launch a new iPhone next month in California."

• Q: Where is the iPhone being launched?
  A: In California.
• Q: What is being launched?
  A: A new iPhone.
• Q: Who is launching the product?
  A: Apple.

📌 Final Thoughts

NLP pipelines and models like spaCy, BERT, GPT, and T5 automate the entire workflow — enabling machines to read, understand, and respond intelligently to human language.