Artificial Intelligence
Natural Language Processing
This guide helps you get your bearings in Natural Language Processing before you start exploring the interactive timeline, framework graph, and concept maps.
Before You Dive In
- The field has gone through three eras: rule-based systems (1960s–80s), statistical methods (1990s–2010s), and deep learning / transformers (2017–present).
- If you're entering now, start with the transformer architecture — it's the basis for GPT, BERT, and nearly all modern NLP.
- Classical NLP tasks (parsing, POS tagging, named entity recognition) haven't disappeared — they're now solved as subtasks within large models.
- Understanding tokenization (how text becomes numbers) is more important than most beginners realize.
Key Terms to Know
TransformerNeural architecture based on self-attention; the basis of modern NLP since 2017.
TokenizationSplitting text into subword units that a model can process numerically.
EmbeddingDense vector representation of a word or sentence in continuous space.
Fine-tuningAdapting a pretrained model to a specific task with additional training.
Attention mechanismLets a model weigh which parts of the input are relevant to each part of the output.
Common Confusions
"NLP is just chatbots" — it spans machine translation, summarization, information extraction, sentiment analysis, and more.
Assuming bigger models are always better — task-specific fine-tuned models often outperform general large models.
Confusing language understanding with language generation — they're related but involve different capabilities and challenges.
Recommended Reading
Speech and Language Processing— Dan Jurafsky & James H. Martin
2024Attention Is All You Need— Vaswani et al.
2017Natural Language Processing with Transformers— Lewis Tunstall, Leandro von Werra & Thomas Wolf
2022How to Use the Interactive View
1
Explore the timeline
Open the interactive view and scan the framework timeline. Which frameworks came first? Which ones overlap? Where are the big transitions?
2
Read the articles
Click into individual frameworks to read what each one claims, where it came from, and how it relates to its neighbors.
3
Check the concept map
See how the key ideas within a framework connect. This is useful for figuring out what to learn first and what depends on what.
4
Test yourself
Take the quiz for any framework you've read about. It's a quick way to find out whether you actually understood the core ideas or just skimmed them.