Language Data and Classical NLP
For readers who need a rigorous, practical foundation in text processing, statistical language modeling, search, and distributional meaning.
Part of Language AI Handbook. The online chapters are free to read; the PDF edition is yours to keep, with future updates included.
Become a more confident language AI practitioner.
Build the understanding to explain how language models work and make more informed choices about using them. The handbook connects the foundations to the methods behind modern language AI, with explanations you can return to as you learn.
See what you’ll learnWhat you will learn
- Understand Text as Data.
- Understand Classical Text Representations.
- Understand Distributional Semantics.
Who this volume is for
- Engineers, researchers, students, AI enthusiasts, linguists, product managers, and anyone interested in understanding or building modern language AI systems, from foundational NLP to advanced large language models.
Prerequisites: No NLP background required; basic Python and statistics help.
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12 five-star reader reviews
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Meet the community“Really appreciate the way you have explained the concepts. Simple to understand, yet builds up knowledge exponentially, all while reinforcing it with examples repeatedly. I've read the last two chapters `74.Self-attention & 75.Q,K,V` and I've to admit, my concepts have never been clearer. You really are doing an amazing job breaking down maths and complex concepts like a story!”
“I would like to thank you for your work. Specifically, the chapter: 'LIME Explainability: Complete Guide to Local Interpretable Model-Agnostic Explanations' was extremely helpful for me in understanding LIME and communicating to colleagues. Your work is inspiring and valuable. Thank you for doing it.”
“I am enjoying reading your writings. I appreciate the effort you put into it. I want to drop a note of thanks. Thank you!”
“Your writing feels like a treasure trove of great material. So, thank you for compiling everything on your website!”
“I found your book Language AI Handbook's explanations of Transformer architectures and production deployment strategies to be exceptionally clear and insightful”
“Thanks so much for establishing such an amazing community. I appreciate the opportunity to be a learner and a contributor to the community.”
“Thank you Michael. It's top!”
“I came across your collection of online handbooks covering quantitative finance, AI, and machine learning. I wanted to reach out and thank you for making such high-quality material openly available. They are incredibly helpful for my CFA Level II review and for brushing up ahead of ML interviews.”
“I came across one of your books, Language AI Handbook, and man, I'm loving it. I've read many books but have never found such a comprehensive book on large models that covers everything from start to finish with such depth.”
“I just came across your website where your books are available for free to read. Thank you so much for making this available to the public. This will help a lot of people.”
“Thanks for putting cool stuff out into the world.”
“I read a few of your blogs on NLP and oh WOW, they really are something, now I exactly know how all of the terms Entropy, Cross-entropy and how it is connected to Perplexity, will definitely read more of your blogs.”
Read all 12 reader reviews
“I would like to thank you for your work. Specifically, the chapter: 'LIME Explainability: Complete Guide to Local Interpretable Model-Agnostic Explanations' was extremely helpful for me in understanding LIME and communicating to colleagues. Your work is inspiring and valuable. Thank you for doing it.”
“Really appreciate the way you have explained the concepts. Simple to understand, yet builds up knowledge exponentially, all while reinforcing it with examples repeatedly. I've read the last two chapters `74.Self-attention & 75.Q,K,V` and I've to admit, my concepts have never been clearer. You really are doing an amazing job breaking down maths and complex concepts like a story!”
“Thanks so much for establishing such an amazing community. I appreciate the opportunity to be a learner and a contributor to the community.”
“I just came across your website where your books are available for free to read. Thank you so much for making this available to the public. This will help a lot of people.”
“I am enjoying reading your writings. I appreciate the effort you put into it. I want to drop a note of thanks. Thank you!”
“Thank you Michael. It's top!”
“Thanks for putting cool stuff out into the world.”
“I found your book Language AI Handbook's explanations of Transformer architectures and production deployment strategies to be exceptionally clear and insightful”
“Your writing feels like a treasure trove of great material. So, thank you for compiling everything on your website!”
“I came across your collection of online handbooks covering quantitative finance, AI, and machine learning. I wanted to reach out and thank you for making such high-quality material openly available. They are incredibly helpful for my CFA Level II review and for brushing up ahead of ML interviews.”
“I read a few of your blogs on NLP and oh WOW, they really are something, now I exactly know how all of the terms Entropy, Cross-entropy and how it is connected to Perplexity, will definitely read more of your blogs.”
“I came across one of your books, Language AI Handbook, and man, I'm loving it. I've read many books but have never found such a comprehensive book on large models that covers everything from start to finish with such depth.”
Exact contents
18 chapters across 3 parts
Every linked chapter is free to read online.
Part I: Text as Data
- 01Character Encoding
Covers ASCII origins and 7-bit limitations, Unicode code points and planes, UTF-8 variable-width encoding scheme, byte order marks and endianness, encoding detection heuristics, common encoding errors and mojibake, practical encoding/decoding in Python.
- 02Text Normalization
Covers Unicode normalization forms (NFC, NFD, NFKC, NFKD), case folding vs lowercasing, accent and diacritic handling, whitespace normalization, ligature expansion, full-width to half-width conversion, implementing a normalization pipeline.
- 03Regular Expressions
Covers regex syntax and metacharacters, character classes and quantifiers, grouping and backreferences, lookahead and lookbehind assertions, greedy vs lazy matching, common NLP patterns (emails, URLs, dates), regex performance considerations.
- 04Sentence Segmentation
Covers period disambiguation challenges, abbreviation handling, rule-based boundary detection, Punkt sentence tokenizer algorithm, evaluation metrics for segmentation, handling edge cases (quotes, parentheses, lists), multilingual segmentation issues.
- 05Word Tokenization
Covers whitespace tokenization limitations, punctuation handling rules, contractions and clitics, language-specific challenges (Chinese, Japanese, German compounds), Penn Treebank tokenization standard, building a rule-based tokenizer, tokenization evaluation.
Part II: Classical Text Representations
- 01Bag of Words
Covers document-term matrix construction, vocabulary building from corpus, word counting and frequency vectors, sparse matrix representation (CSR/CSC formats), vocabulary pruning (min_df, max_df), binary vs count representations, limitations of word order loss.
- 02N-grams
Covers bigram and trigram extraction, n-gram vocabulary explosion, n-gram frequency distributions, Zipf's law in n-grams, character n-grams for robustness, skip-grams and flexible windows, n-gram indexing for search.
- 03N-gram Language Models
Covers Markov assumption and chain rule, maximum likelihood estimation, probability calculation for sequences, handling unseen n-grams, start and end tokens, generating text from n-gram models, model storage and lookup efficiency.
- 04Smoothing Techniques
Covers add-one (Laplace) smoothing, add-k smoothing and tuning, Good-Turing smoothing derivation, Kneser-Ney smoothing intuition and formula, interpolation vs backoff, modified Kneser-Ney, comparing smoothing methods empirically.
- 05Perplexity
Covers cross-entropy definition and derivation, perplexity as branching factor, relationship to bits-per-character, held-out evaluation methodology, perplexity vs downstream performance, comparing models with perplexity, perplexity limitations and caveats.
- 06Term Frequency
Covers raw term frequency, log-scaled term frequency, boolean term frequency, augmented term frequency, L2-normalized frequency vectors, term frequency sparsity patterns, efficient term frequency computation.
- 07Inverse Document Frequency
Covers document frequency calculation, IDF formula derivation, IDF intuition (rare words matter more), smoothed IDF variants, IDF across corpus splits, relationship to information theory, implementing IDF efficiently.
- 08TF-IDF
Covers TF-IDF formula and variants, TF-IDF vector computation, TF-IDF normalization options, BM25 as TF-IDF extension, document similarity with TF-IDF, TF-IDF for feature extraction, sklearn TfidfVectorizer deep dive.
- 09BM25
Covers BM25 derivation from probabilistic IR, saturation parameter k1, length normalization parameter b, BM25+ and BM25L variants, field-weighted BM25, implementing BM25 scoring, BM25 vs TF-IDF empirically.
Part III: Distributional Semantics
- 01The Distributional Hypothesis
Covers Firth's "you shall know a word by the company it keeps," distributional similarity intuition, context window definitions, paradigmatic vs syntagmatic relations, word similarity from distributions, limitations of distributional semantics.
- 02Co-occurrence Matrices
Covers word-word co-occurrence matrices, word-document matrices, context window size effects, weighting by distance, symmetric vs directional contexts, matrix sparsity patterns, efficient construction algorithms.
- 03Pointwise Mutual Information
Covers PMI formula derivation, PMI interpretation as association, positive PMI (PPMI), shifted PPMI variants, PMI matrix properties, PMI vs raw counts comparison, PMI for collocation extraction.
- 04Singular Value Decomposition
Covers SVD mathematical formulation, truncated SVD for dimensionality reduction, LSA (Latent Semantic Analysis), choosing embedding dimensions, SVD computational complexity, randomized SVD for scale, interpreting SVD dimensions.
Choose the edition that fits your next step
Volume 1: Language Data and Classical NLP
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Get all-volume accessExplore the other volumes
Volume 2 · 790 pages
Embeddings, Tokenization, and Sequence Labeling
Word Embeddings · Subword Tokenization · Sequence Labeling
Volume 3 · Coming soon
Linguistic Structure and Meaning
Morphology · Syntax · Semantics · Discourse · Dialogue
Volume 4 · 920 pages
Neural NLP and Sequence Models
Neural Network Foundations · Recurrent Neural Networks · Sequence-to-Sequence
Volume 5 · 860 pages
Transformers from First Principles
Self-Attention · Positional Encoding · Transformer Blocks · Transformer Architectures
Volume 6 · 510 pages
Efficient, Long-Context, and Alternative Architectures
Efficient Attention · Long Context
Volume 7 · 760 pages
Pre-training Data and Scaling
Data Curation · Pre-training Objectives · Scaling Laws
Volume 8 · 440 pages
Encoder, Multilingual, and Translation Models
BERT and Variants · Encoder-Decoder Models
Volume 9 · 730 pages
Decoder Language Models and Capabilities
GPT Architecture · Modern Decoder Models · Emergent Capabilities
Volume 10 · 920 pages
Large-Scale Training and Sparse Models
Training Infrastructure · Training Optimization · Mixture of Experts
Volume 11 · 730 pages
Fine-tuning and Efficient Adaptation
Fine-tuning Fundamentals · Parameter-Efficient Fine-tuning · Instruction Tuning
Volume 12 · 510 pages
Alignment, Preferences, and Oversight
Alignment and RLHF
Volume 13 · 220 pages
Reasoning and Verifiable Learning
Reasoning
Volume 14 · 640 pages
Efficient Inference and On-Device Models
Model Compression · Inference Optimization
Volume 15 · 600 pages
Retrieval, Knowledge, and Memory
Retrieval-Augmented Generation · Continual Learning
Volume 16 · 510 pages
Agents and AI Coding Systems
Code Generation · Tool Use and Agents
Volume 17 · 540 pages
Speech, Multimodal, and Embodied Language
Multimodal Models · Speech and Audio
Volume 18 · 950 pages
Evaluation, Factuality, and Reliability
Evaluation Fundamentals · Benchmark Evaluation · Human and Model Evaluation · Hallucination and Factuality
Volume 19 · 760 pages
Responsible, Secure, and Interpretable Language AI
Bias and Fairness · Interpretability · Safety and Security
Volume 20 · 510 pages
Applications, Production, and Compound AI Systems
LLM Applications · Production Systems
Author and edition details
About the author and this edition
Michael Brenndoerfer
Michael has spent more than a decade working across software engineering, data, AI, and business. He writes to understand difficult ideas more deeply and to share what he learns in a clear, practical way.
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- First edition
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Kept current, not frozen in time
Library access includes every future volume and update. When a volume changes, its updated PDF appears in My books at no extra cost.
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Edition 2026.08.0
Initial volume-library release with 19 written PDFs, a Volume 3 placeholder, and no combined edition.