What is Natural Language Processing ?

Nlp tutorial · PySpark.in

Chapter Objective

• Understand what NLP is
• Understand the problem NLP solves
• See where NLP fits in AI systems

1. What is Natural Language Processing?

• Humans communicate using language: text and speech.
• Machines work with numbers and structured data.
• Natural Language Processing (NLP) is the field that enables machines to work with human language.

Definition:
NLP is a branch of Artificial Intelligence focused on enabling computers to read, interpret, and process human language.

Core idea:
NLP converts language into a form machines can understand.

2. Why Do Machines Need NLP?

• Text data is unstructured.
• Words can have multiple meanings.
• Meaning depends on context and order.
• Machines cannot understand language directly.

Without NLP, computers only see text as raw characters.

3. What Problems Does NLP Solve?

NLP allows machines to:

• Identify important words and patterns
• Understand intent and sentiment
• Categorize and organize text
• Extract useful information from documents

These capabilities power many real-world systems.

4. Examples of NLP in Everyday Life

• Search engines understanding queries
• Email spam filtering
• Chatbots responding to messages
• Sentiment analysis on reviews
• Text classification in documents

5. NLP in the AI Landscape

• Artificial Intelligence: Systems that mimic human intelligence
• Machine Learning: Systems that learn from data
• Deep Learning: Neural network-based learning
• NLP: Language-focused intelligence

NLP often uses machine learning and deep learning, but its core challenge is understanding language.

6. How an NLP System Works (High Level)

Most NLP systems follow a simple flow:

1. Input text
2. Language processing
3. Model analysis
4. Output or decision

What you learned :

• NLP helps machines understand human language.
• Language is complex and ambiguous.
• NLP transforms text into structured representations.
• Understanding NLP starts with understanding language itself.

Before machines can make sense of text, we need to understand what makes language complex and how to represent it in a way computers can work with

More Nlp tutorials

• Natural Language Understanding (NLU) , Natural Language Generation (NLG) and phases of NL
• Tokenization in NLP and NLP Project Life Cycle
• Coverting The Text to Vector(one hot encoding and bag of words method)
• Convert text to vector: N-grams and TF-IDF method
• Word Embedding
• Working with Text in NLP

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