https://web.stanford.edu/~jurafsky/slp3/

    Speech and Language Processing (3rd ed. draft)
    Dan Jurafsky and James H. Martin

    Here's our Feb 3, 2024 release! We also expect to release Chapter 12
    soon in an updated release.

    Individual chapters and updated slides are below; here is a single
    pdf of all the chapters in the Feb 3, 2024 release!

    Feel free to use the draft chapters and slides in your classes, the
    resulting feedback we get from you makes the book better!
    As always, typos and comments 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)

    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!

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

    If you need last year's Jan 2023 draft chapters, they are here;

     
        Chapter                                   Slides
        Part I: Fundamental Algorithms
    1:  Introduction
                                                  2: Text Processing [
    2:  Regular Expressions, Text Normalization,  pptx] [pdf]
        Edit Distance                             2: Edit Distance [pptx]
                                                  [pdf]
    3:  N-gram Language Models                    3: [pptx] [pdf]
    4:  Naive Bayes, Text Classification, and     4: [pptx] [pdf]
        Sentiment
    5:  Logistic Regression                       5: [pptx] [pdf]
    6:  Vector Semantics and Embeddings           6: [pptx] [pdf]
    7:  Neural Networks and Neural Language       7: [pptx] [pdf]
        Models
    8:  Sequence Labeling for Parts of Speech and 8: (Intro only) [pptx]
        Named Entities                            [pdf]
    9:  RNNs and LSTMs
    10: Transformers and Large Language Models
    11: Fine-tuning and Masked Language Models
    12: Prompting, In-Context Learning, and
        Instruct Tuning
     
        Part II: NLP Applications
    13: Machine Translation
    14: Question Answering and Information
        Retrieval
    15: Chatbots and Dialogue Systems             15 [pptx] [pdf]
    16: Automatic Speech Recognition and
        Text-to-Speech
     
        Part III: Annotating Linguistic Structure
    17: Context-Free Grammars and Constituency
        Parsing
    18: Dependency Parsing
    19: Information Extraction: Relations,
        Events, and Time
    20: Semantic Role Labeling and Argument
        Structure
    21: Lexicons for Sentiment, Affect, and
        Connotation
    22: Coreference Resolution
    23: 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