Rule-Based NLP with NLP++: A practical guide to rule-based text analysis with VisualText (English Ed

Rule-Based NLP with NLP++ is a comprehensive guide to building accurate, transparent, and maintainable text analysis systems using NLP++, the only programming language created specifically for natural language processing. Authored by the creators of NLP++ and the VisualText IDE, this book guides readers through the full spectrum of rule-based NLP development, from foundational concepts to production-grade analyzers.

The book introduces the NLP++ pipeline architecture and its uniform, glass-box approach to text analysis, in contrast to opaque statistical and ML methods. Using the VisualText IDE, readers will learn to design and build multi-pass text analyzers, write grammar rules and functions, and manage hierarchical knowledge bases. Practical chapters explore example analyzers for date-time recognition, formatted text analysis, and named entity extraction with coreference resolution. An advanced English language analyzer is also documented in detail. Integration chapters cover calling NLP++ analyzers from Python, TypeScript, C++, and the HPCC Systems supercomputing platform.

Whether you are a computational linguist, software engineer, or student of NLP, this book equips you with the tools and thinking needed to build explainable, customizable, and high-performing text analysis systems that emulate human language processing.

WHAT YOU WILL LEARN

● Design multi-pass NLP++ analyzers using VisualText IDE.

● Learn to write rules and manage the parse tree and knowledge base.

● Solve real-world challenges like NER and coreference.

● Integrate analyzers into Python, C++, and HPCC Systems.

● Compare NLP++ to statistical, mission-critical AI methods.

WHO THIS BOOK IS FOR

This book is for software engineers, computational linguists, NLP researchers, and students who want to build accurate and explainable text analysis systems. Readers should have basic programming experience, ideally in Python, Typescript, or C++. No prior NLP knowledge is required.