Natural Language Processing (NLP)

Natural Language Processing (NLP) is the branch of AI that enables computers to understand, interpret, and generate human language. It bridges the gap between human communication and computer understanding.

NLP enables:

• Chatbots and virtual assistants
• Machine translation
• Sentiment analysis
• Text summarization
• Question answering
• Content generation
• Speech recognition

The challenge: Human language is complex, ambiguous, and context-dependent. "I saw her duck" could mean seeing a bird or seeing someone ducking. NLP systems must handle this complexity.

Modern LLMs like GPT-4 and Claude represent the current pinnacle of NLP, capable of understanding and generating human-like text across virtually any topic.

Understanding tasks:

Sentiment analysis: Determine if text is positive, negative, or neutral. "This product is amazing!" → Positive

Named entity recognition (NER): Identify people, places, organizations. "Apple CEO Tim Cook announced..." → ORG: Apple, PERSON: Tim Cook

Part-of-speech tagging: Label grammatical roles: noun, verb, adjective, etc.

Text classification: Categorize text into predefined categories. Email → Spam/Not Spam

Generation tasks:

Text generation: Produce human-like text from prompts.

Machine translation: Convert text between languages.

Summarization: Condense long text into key points.

Question answering: Generate answers from context.

Rule-based era (1950s-1990s): Hand-coded grammar rules and dictionaries. Brittle, limited vocabulary.

Statistical NLP (1990s-2010s): Learn patterns from data. Hidden Markov Models, Naive Bayes. Better but still limited.

Neural NLP (2013-2017): Word embeddings (Word2Vec), RNNs, LSTMs. Breakthrough in language understanding.

Transformer era (2017+): Attention mechanism revolutionized NLP. BERT, GPT, and successors.

Large Language Models (2020+): GPT-3, ChatGPT, Claude, Gemini. Emergent capabilities from scale.

Key milestones:

• 2013: Word2Vec—words as vectors
• 2017: Transformer—"Attention Is All You Need"
• 2018: BERT—bidirectional understanding
• 2020: GPT-3—175B parameters, few-shot learning
• 2022: ChatGPT—conversational AI goes mainstream
• 2023+: Multimodal models, agents

Tokenization: Split text into units (words, subwords, characters). Foundation of NLP processing.

Stemming/Lemmatization: Reduce words to base forms. "running" → "run"

Word embeddings: Represent words as dense vectors capturing meaning. Similar words have similar vectors.

Attention: Allow models to focus on relevant parts of input. Revolutionary for sequence processing.

Transfer learning: Pre-train on large corpus, fine-tune for specific tasks. Enables powerful models with less data.

Prompt engineering: Craft inputs that elicit desired outputs from LLMs. New skill in the LLM era.

In-context learning: LLMs learn from examples in the prompt without parameter updates.

Consumer applications:

• Voice assistants (Siri, Alexa)
• Autocomplete and smart compose
• Language translation
• Search engines
• Chatbots

Business applications:

• Customer support automation
• Document processing
• Email filtering
• Social media monitoring
• Content moderation
• Legal document analysis

Specialized applications:

• Medical records processing
• Financial news analysis
• Academic research tools
• Accessibility (text-to-speech)
• Code generation

Emerging applications:

• AI writing assistants
• Conversational AI
• Autonomous agents
• Multimodal understanding

Ambiguity: Same words can have different meanings. "Bank" = financial institution or river edge?

Context: Understanding requires knowing what came before and broader world knowledge.

Sarcasm and irony: "Great, another meeting" isn't positive.

Multilingual: Different languages have different structures, idioms, and cultural contexts.

Low-resource languages: Most NLP research focuses on English. Many languages lack training data.

Hallucination: LLMs generate plausible but false information.

Bias: Models reflect biases in training data.

Evaluation: Hard to measure if generated text is "good."

Reasoning: Understanding words vs. understanding meaning and logic.