Natural Language Processing (NLP)

ELI5 (Explain like I’m 5)

Imagine you move to a new country where everyone speaks a language you don’t know.

At first, all you hear is noise. Then you start recognizing common words like coffee, sandwich, railway station, etc. Slowly, you understand sentences. Eventually, you can figure out what people mean, even when they crack jokes or non-standard phrases.

Natural Language Processing is about teaching computers to go through that same journey.

Instead of just seeing letters and sounds, the system learns how words fit together, what sentences mean, and how context changes everything.

How does Modern NLP work?

Early NLP relied on strict, hand-written rules and dictionaries. However, human language is messy and full of slang and context.

Modern NLP uses deep learning and neural networks to learn language by analyzing large amounts of text. This allows the AI to predict the next word in a sentence or understand context across long paragraphs. 

NLP sits at the intersection of linguistics and computer science. It involves several complex layers, ranging from basic syntax (the structure of sentences) to semantics (the actual meaning) and pragmatics (the intent behind the words).

NLU vs. NLG

NLP is generally divided into two main components:

Natural Language Understanding (NLU)

This is the reading part. It focuses on breaking down human language to determine what the user actually means. It handles tasks like sentiment analysis (figuring out if a review is happy or upset) and named entity recognition (identifying people, places, or dates).

Natural Language Generation (NLG)

This is the writing side. It takes structured data or ideas and turns them into human-readable text. When an AI writes a summary of a meeting or answers a prompt in Geekflare Connect, it is using NLG.

Real-World Applications of NLP

Common tasks performed by NLP include:

• Text classification: spam detection, sentiment analysis, topic labeling.
• Information extraction: pulling entities (company names), relationships, or key facts.
• Machine translation & summarization: converting languages, compressing long text.
• Question answering: responding using a knowledge source or context.
• Virtual Assistants: Like Siri or Alexa, which interpret voice commands.

Where NLP can go wrong

NLP systems can:

• Misread context (sarcasm, ambiguity, cultural nuance)
• Hallucinate (generate convincing but factually incorrect text)
• Inherit bias from training data

That’s why evaluation, guardrails, and human review are essential.