TL;DR

Natural Language Processing (NLP) enables AI to understand and generate human language. Core techniques include tokenization, embeddings, transformers, and attention mechanismsā€”powering chatbots, translation, and more.

What is NLP?

NLP is a branch of AI focused on the interaction between computers and human language. It combines linguistics, computer science, and machine learning to teach computers to:

  • Understand meaning
  • Extract information
  • Generate text
  • Translate languages

Key NLP tasks

Text classification: Categorizing text (spam detection, sentiment analysis)
Named entity recognition: Finding names, places, dates in text
Machine translation: Converting between languages
Question answering: Extracting answers from text
Text summarization: Condensing long documents
Text generation: Creating human-like text

Core NLP concepts

Tokenization:

  • Breaking text into units (words or sub-words)
  • "Hello world" ā†’ ["Hello", "world"]
  • Crucial first step in processing

Embeddings:

  • Converting words to numbers (vectors)
  • Similar words have similar vectors
  • Captures meaning mathematically

Part-of-speech tagging:

  • Labeling words (noun, verb, adjective)
  • Helps understand sentence structure

Syntax and parsing:

  • Analyzing grammatical structure
  • Building parse trees

Semantic analysis:

  • Understanding meaning beyond words
  • Context and intent matter

The transformer revolution

Pre-transformer NLP:

  • Processed text sequentially (slow)
  • Struggled with long-range dependencies
  • Limited context understanding

Transformer models (2017+):

  • Process entire text at once (parallel)
  • Attention mechanism weights important words
  • Handles long contexts effectively
  • Powers GPT, BERT, and modern LLMs

How LLMs use NLP

Training phase:

  1. Tokenize billions of words
  2. Learn statistical patterns
  3. Build embeddings
  4. Optimize for next-word prediction

Inference (using the model):

  1. Tokenize user input
  2. Convert to embeddings
  3. Process through transformer layers
  4. Generate probability distribution for next word
  5. Sample and repeat

Common NLP applications

Customer service: Chatbots, intent classification, sentiment analysis
Content moderation: Detecting hate speech, spam, harmful content
Search: Query understanding, document ranking
Healthcare: Clinical note analysis, diagnosis assistance
Legal: Contract analysis, case law research
Finance: News sentiment, fraud detection

Challenges in NLP

  • Ambiguity ("I saw her duck")
  • Context dependency ("bank" = river or money?)
  • Sarcasm and humor
  • Cultural nuances
  • Low-resource languages
  • Domain-specific jargon

NLP evaluation metrics

  • Accuracy: % of correct predictions
  • Precision: Correctness of positive predictions
  • Recall: Coverage of actual positives
  • F1 score: Balance of precision and recall
  • BLEU score: Translation quality
  • Perplexity: Language model confidence

What's next

  • Embeddings Explained
  • Transformer Architecture
  • Prompt Engineering