Home / Jurafsky

Speech and Language Processing Notes

Jurafsky

Speech and Language Processing Notes

These notes are based on "Speech and Language Processing" by Dan Jurafsky and James H. Martin — the definitive textbook for Natural Language Processing. This guide makes NLP concepts accessible to undergraduates while building the foundation for advanced study.

What is Natural Language Processing?

NLP is the field of computer science focused on enabling computers to understand, interpret, and generate human language. It bridges linguistics, computer science, and machine learning.

Why is language hard for computers?

  • Language is ambiguous (bank of a river vs. bank for money)
  • Language is context-dependent (meaning changes with context)
  • Language is creative (infinite sentences from finite vocabulary)
  • Language has implicit knowledge (common sense, world knowledge)

Topics Covered

Chapter Topic What You'll Learn
2 Regular Expressions & Text Processing Pattern matching, tokenization, normalization
3 N-Grams & Language Models Probabilistic models of word sequences
6 Vector Semantics Word embeddings, similarity, Word2Vec
9 Sequence Models RNNs, LSTMs, and attention mechanisms
10 Encoder-Decoder Models Seq2Seq, machine translation
11 Transfer Learning BERT, pre-training, fine-tuning

The Evolution of NLP

Rule-based    →    Statistical    →    Neural    →    Pre-trained
(1950s-1980s)      (1990s-2000s)     (2013-2018)    (2018-present)

Hand-crafted       Probabilistic      Deep learning   Transformers
grammars          models (HMMs,       (RNNs, LSTMs)   (BERT, GPT)
                  n-grams)

Core NLP Tasks

Understanding Language:

  • Text classification (spam detection, sentiment)
  • Named entity recognition (finding names, places, dates)
  • Parsing (sentence structure)
  • Semantic analysis (meaning extraction)

Generating Language:

  • Machine translation
  • Text summarization
  • Question answering
  • Dialogue systems

Prerequisites

  • Programming: Python, basic data structures
  • Math: Probability basics, linear algebra fundamentals
  • ML Basics: Helpful but not strictly required

How to Use These Notes

  1. Start with fundamentals: Regex and n-grams build the foundation
  2. Understand the progression: From sparse to dense representations, from RNNs to Transformers
  3. Connect to practice: Try implementing concepts in Python
  4. See the big picture: Modern NLP combines all these ideas

Let's dive into the fascinating world of language and computation!