# A Survey and Classification of Controlled Natural Languages

arXiv: https://arxiv.org/abs/1507.01701 · HTML: https://mlsanity.com/paper/1507.01701/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2015.

## Built on by (most cited first, 18 of 18)

- [A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks](https://mlsanity.com/paper/2306.07303/) (2023)
- [LLASP: Fine-tuning Large Language Models for Answer Set Programming](https://mlsanity.com/paper/2407.18723/) (2024)
- [Towards Formal Verification of LLM-Generated Code from Natural Language Prompts](https://mlsanity.com/paper/2507.13290/) (2025)
- [Towards Automatic Composition of ASP Programs from Natural Language Specifications](https://mlsanity.com/paper/2403.04541/) (2024)
- [CNL2ASP: converting controlled natural language sentences into ASP](https://mlsanity.com/paper/2311.10505/) (2023)
- [OntoLearner: A Modular Python Library for Ontology Learning with Large Language Models](https://mlsanity.com/paper/2607.01977/) (2026)
- [SpeciaLex: A Benchmark for In-Context Specialized Lexicon Learning](https://mlsanity.com/paper/2407.13297/) (2024)
- [Understanding Pre-Editing for Black-Box Neural Machine Translation](https://mlsanity.com/paper/2102.02955/) (2021)
- [HyperPIE: Hyperparameter Information Extraction from Scientific Publications](https://mlsanity.com/paper/2312.10638/) (2023)
- [An NLG pipeline for a legal expert system: a work in progress](https://mlsanity.com/paper/2107.02421/) (2021)
- [Trustworthy Formal Natural Language Specifications](https://mlsanity.com/paper/2310.03885/) (2023)
- [Natural Language Generation](https://mlsanity.com/paper/2503.16728/) (2025)
- [When Prompt Engineering Meets Software Engineering: CNL-P as Natural and Robust "APIs'' for Human-AI Interaction](https://mlsanity.com/paper/2508.06942/) (2025)
- [Bridging Pedagogy and Play: Introducing a Language Mapping Interface for Human-AI Co-Creation in Educational Game Design](https://mlsanity.com/paper/2603.03644/) (2026)
- [Open Machine Translation for Esperanto](https://mlsanity.com/paper/2603.29345/) (2026)
- [BLAST: Benchmarking LLMs with ASP-based Structured Testing](https://mlsanity.com/paper/2604.22306/) (2026)
- [Ceci n'est pas une pipe: AI systems as semantic abstractions](https://mlsanity.com/paper/2607.09489/) (2026)
- [Transcript-Managed Transformers: Monotone Multi-Agent Collapse and Universality with Two Pop-Enabled Transcripts](https://mlsanity.com/paper/2607.29496/) (2026)

## Abstract (arXiv)

What is here called controlled natural language (CNL) has traditionally been given many different names. Especially during the last four decades, a wide variety of such languages have been designed. They are applied to improve communication among humans, to improve translation, or to provide natural and intuitive representations for formal notations. Despite the apparent differences, it seems sensible to put all these languages under the same umbrella. To bring order to the variety of languages, a general classification scheme is presented here. A comprehensive survey of existing English-based CNLs is given, listing and describing 100 languages from 1930 until today. Classification of these languages reveals that they form a single scattered cloud filling the conceptual space between natural languages such as English on the one end and formal languages such as propositional logic on the other. The goal of this article is to provide a common terminology and a common model for CNL, to contribute to the understanding of their general nature, to provide a starting point for researchers interested in the area, and to help developers to make design decisions.
