Speech and Language Processing (3rd ed. draft)
Speech and Language Processing




Speech and Language Processing
(3rd ed. draft)

Dan Jurafsky and James H. Martin




Here's our August 20, 2024 release!  




Individual chapters and updated slides are below;

Here is a single pdf of Aug 20, 2024 book! 


Feel free to use the draft chapters and slides in your classes, print it out, whatever, the resulting feedback we get from you makes the book better!

Typos and comments are very welcome (just email slp3edbugs@gmail.com
and let us know the date on the draft)!
(Don't bother reporting missing refs due to cross-chapter cross-reference problems  in the indvidual chapter pdfs, those are fixed in the full book draft)

Gratitude! We've put up a list here of the amazing people who have sent so many fantastic suggestions and bug-fixes for improving the book.
We are really grateful to all of you for your help, the book would not be possible without you!

How to cite the book:

Daniel Jurafsky and James H. Martin. 2024.  Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models, 3rd edition. Online manuscript released August 20, 2024. https://web.stanford.edu/~jurafsky/slp3.

A  bib entry for the book is here.

When will the book be finished?  Don't ask.  

If you need the previous Feb 2024 draft chapters, 
they are here;



 


Chapter
Slides 


Part I: Fundamental Algorithms

1:Introduction
2:
        Regular Expressions, Tokenization, Edit Distance

2: Text Processing
[pptx] 
[pdf]
2: Edit Distance
[pptx] 
[pdf]

3: 
        N-gram Language Models

3: [pptx] 
[pdf]

4: 
         Naive Bayes, Text Classification,  and Sentiment 

4: [pptx] 
[pdf]

5: 
         Logistic Regression

5: [pptx] 
[pdf]

6: 
         Vector Semantics and Embeddings

    6: [pptx] 
[pdf]

7: 
        Neural Networks 
        
    7: [pptx] [pdf]

8: 
        
        RNNs and LSTMs 
9: 
        Transformers  

9: [pptx] 
[pdf]

10: 
        Large Language Models 
10: [pptx] 
[pdf]
11: Masked Language Models 
12: 
        Model Alignment, Prompting, and In-Context Learning

 
Part II: NLP Applications

13: Machine Translation 
14: Question Answering, Information Retrieval, and RAG 

15: Chatbots and Dialogue Systems 
     
15 [pptx] 
[pdf]
    

16: Automatic Speech Recognition and Text-to-Speech


 
Part III: Annotating Linguistic Structure
17: 
        Sequence Labeling for Parts of Speech and Named Entities

    17: (Intro only) [pptx] [pdf]


18: Context-Free Grammars and Constituency Parsing
19: Dependency Parsing 
20: Information Extraction: Relations, Events, and Time
21: Semantic Role Labeling and Argument Structure
22: Lexicons for Sentiment, Affect, and Connotation 
23: Coreference Resolution and Entity Linking
24: Discourse Coherence
 
Appendix Chapters (will be just on the web)
A: Hidden Markov Models 
B: Spelling Correction and the Noisy Channel 
C: Statistical Constituency Parsing 
D: Context-Free Grammars 
E: Combinatory Categorial Grammar 
F: Logical Representations of Sentence Meaning 
G: Word Senses and WordNet 
H: Phonetics 


