37.2243, Books: Rule-based NLP With NLP++: de Hilster & Meyers (2026)

Thu Jul 2 11:05:02 UTC 2026

LINGUIST List: Vol-37-2243. Thu Jul 02 2026. ISSN: 1069 - 4875.

Subject: 37.2243, Books: Rule-based NLP With NLP++: de Hilster & Meyers (2026)

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Date: 02-Jul-2026
From: David de Hilster [d.dehilster at northeastern.edu]
Subject: Rule-based NLP With NLP++: de Hilster & Meyers (2026)

Title: Rule-based NLP With NLP++
Subtitle: A Practical Guide to Rule-based Text Analysis With
Visualtext
Publication Year: 2026

Publisher: BPB Publications
           https://bpbonline.com/
Book URL:
https://bpbonline.com/products/rule-based-nlp-with-nlp?_pos=1&_sid=29431535e&_ss=r%3fvariant=45061156405448

Author(s): David de Hilster, Amnon Meyers

ISBN: 9789365891980
eISBN: 9789365890082

Abstract:

Overview:
"Rule-Based NLP with NLP++" is the first textbook devoted to NLP++,
the only general-purpose programming language created specifically for
natural language processing. Written by the co-creators of the
language, David de Hilster and Amnon Meyers, the book teaches
rule-based, knowledge-driven text analysis from the ground up using
the VisualText development environment and its NLP Engine. It presents
NLP++ as a transparent, "glass-box" alternative to statistical,
neural, and machine-learning approaches, emphasizing analyzers whose
outputs can be explained, justified, debugged, and enhanced without
limitation.
Approach:
The book teaches by building. Rather than presenting the language in
the abstract, it walks the reader through the development of several
complete sample analyzers, covering NLP++ syntax, actions, functions,
and online code repositories along the way. Central to the approach is
the multi-pass pipeline architecture, in which a text analyzer is
constructed as a sequence of passes that progressively elaborate a
single best-first parse tree, working in tandem with the Conceptual
Grammar (CG) hierarchical knowledge base management system. The
authors situate NLP++ historically against earlier and contemporary
systems, including Stanford CoreNLP, NLTK, GATE, and finite-state and
pattern-based architectures, and frame NLP++ as "the SQL for text," a
universal language for natural language much as SQL is for relational
data.
Chapter Structure:
The textbook is organized into eight chapters with four supporting
appendices. Chapter 1 introduces the background and history of NLP
architectures, the NLP++ language, and its pipeline approach. Chapter
2 details the multi-pass pipeline architecture and the language
itself, including the analyzer sequence, pass files, regions, rules,
code syntax, variables, data types, and operators. Chapter 3 covers
specialized topics such as recursion, dictionaries, knowledge base and
module files, Unicode and emoji handling, and language-specific
functions for languages including Chinese and Nepali. Chapter 4
presents the VisualText IDE: its history, analyzer folder structure,
the freeware VS Code language extension, and its interconnected text,
parse tree, knowledge base, and pass-file views. Chapter 5 explores
the cognitive philosophy behind NLP++ and its code and knowledge
libraries. Chapter 6 covers the practical process of developing text
analyzers, including bottom-up and top-down strategies, ambiguity
handling, and output. Chapter 7 guides the reader through worked
example analyzers, including a date-time analyzer, formatted-text
analysis, entity extraction, and a full English parser. Chapter 8
addresses integrating and deploying analyzers in other environments,
including Python (the NLPPlus package), TypeScript, C++ libraries, and
the HPCC Systems supercomputing platform. Appendices provide resources
and workshop materials, a full NLP++ language reference, miscellaneous
topics, and a complete reference for the full English parser.
Pedagogical Features:
Each chapter includes clearly stated objectives, a chapter structure
overview, a conclusion, and end-of-chapter true/false,
multiple-choice, and short-answer questions with answers, making the
book suitable for classroom use. A downloadable code bundle and
colored images accompany the text, hosted on the publisher's GitHub
repository. The book is designed to support university coursework in
rule-based NLP.
Audience:
The textbook is intended for students, researchers, computational
linguists, and software developers who want to build accurate,
maintainable, and explainable text analysis systems. It assumes
familiarity with general programming concepts; NLP++ resembles the C
programming language in many of its constructs, so readers with a
programming background will find the syntax approachable. No prior
background in natural language processing is required.
About the Authors:
David de Hilster is an adjunct professor at Northeastern University
College of Engineering and co-creator of NLP++ and VisualText. He
holds a bachelor's in mathematics and a master's in linguistics from
Ohio State University, has four decades of experience in NLP, and is
the founder of the Natural Language Understanding Global Initiative.
Amnon Meyers is the co-creator of NLP++ and the creator of the
Conceptual Grammar knowledge base system. He holds a master's in
computer science and a master's in organic chemistry from UC Berkeley
and a bachelor's in biology from MIT, with experience at the McDonnell
Douglas AI Lab, TRW Space Park, and the UC Irvine AI Lab on DARPA and
Navy projects.
Links:
Publisher page: https://book.visualtext.org
Amazon: https://amazon.visualtext.org
NLP++ and VisualText: https://visualtext.org
NLP Foundation: https://nlp.foundation
Natural Language Understanding Global Initiative: https://nluglob.org

Linguistic Field(s): Computational Linguistics

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