C++ for Natural Language Processing

Build and Deploy NLP Systems with C++ and Modern AI

Roland Kessler

• Publisher: Chiify
• Year: 2026
• Topic: Computer Science
• eBook ISBN:
  9798259604285
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

C++ natural language processing and NLP C++ programming. Unlock the raw power of C++ for natural language processing with this hands-on guide. Build fast text-processing tools—tokenizers, search engines, and classifiers—where C++ speed beats interpreted languages. From beginner to pro, master NLP with C++ and create high-performance applications. In this practical book, Roland Kessler shows you how to leverage C++'s unmatched performance for natural language processing tasks. You'll start with tokenization and text normalization, then move to search algorithms and classification models. Each chapter includes complete code examples and real-world projects. Whether you're a data scientist, software engineer, or researcher, you'll learn to build tools that process text faster than Python or Java. What you will learn: Design custom tokenizers for any language Implement efficient search algorithms with C++ STL Build text classifiers using machine learning libraries Optimize memory and CPU for large-scale NLP Integrate with existing C++ projects Unlike general C++ books, this focuses solely on NLP. It's the perfect companion for developers who want speed. Compare with [placeholder] and [placeholder] for a focused approach to text processing. This hands-on C/C++/C#/.NET guide is written to be used at the keyboard: every concept is paired with something you can run, adapt, and keep. You move from first principles to real, working results, with the common errors and fixes called out along the way so you are never stuck for long.

Table of contents

• C++ for Natural Language Processing
• C++ for Natural Language Processing
• Preface
• Chapter 1 — Setting Up Your C++ NLP Development Environment
• Chapter 2 — Modern C++ Essentials for Text Processing
• Chapter 3 — Reading, Writing, and Normalizing Real-World Text
• Chapter 4 — Tokenization: From Characters to Subwords
• Chapter 5 — N-gram Language Models and Text Generation
• Chapter 6 — Vector Space Models and Classic Search
• Chapter 7 — High-Performance Text Classification
• Chapter 8 — Sequence Labeling for Named Entity Recognition
• Chapter 9 — Static Word Embeddings and Analogies
• Chapter 10 — Neural Networks from Scratch in C++
• Chapter 11 — Sequence Modeling with LSTMs
• Chapter 12 — Transformer Inference with ggml and llama.cpp
• Chapter 13 — Building a Complete RAG System
• Chapter 14 — Production: Profiling, Serialization, and Packaging
• Chapter 15 — Capstone: Train Your Own Mini-GPT from Scratch
• Chapter 16 — Advanced Decoding and Sampling Strategies
• Chapter 17 — Putting It All Together: The Unified Assistant
• Chapter 18 — Where to Go Next: The C++ NLP Frontier