Speech and Language Processing by Dan Jurafsky and James H. Martin

A textbook that covers both classical and modern approaches by Daniel Jurafsky is an immortal classic that is constantly updated.

As a supplement, you can also look at the course LSA 311: Computational Lexical Semantics by the same author.

Table of contents:

Part I: Fundamental Algorithms
1: Introduction
2: Regular Expressions, Tokenization, Edit Distance
3: N-gram Language Models
4: Naive Bayes, Text Classification, and Sentiment
5: Logistic Regression
6: Vector Semantics and Embeddings
7: Neural Networks
8: RNNs and LSTMs
9: Transformers
10: Large Language Models
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
16: Automatic Speech Recognition and Text-to-Speech

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

Links:
- Site
- Book (wait, it'll load eventually)

Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #nlp #naturallanguageprocessing #llm #nn

@data_science_weekly