A Survey and Classification of Controlled Natural Languages
By Tobias Kuhn. International Conference on Computational Logic, 2014
385 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/9a62d953b8868b410a4f5dbaf9d49670727660db
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Gordon J. Pace
- Jamal Bentahar
- Witold Pedrycz
- Najat Drawel
- Ahmed Elsebai
- Saidul Islam
- Gaith Rjoub
- M. Rosner
Citations in the corpus
- A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks (2023, background): “This task is crucial for various NLP applications, including language understanding, summarization, and question answering, among others (Chowdhary & Chowdhary, 2020, Kuhn, 2014, Hu et al., 2016).”
- Explaining Violation Traces with Finite State Natural Language Generation Models (2014, background): “An excellent survey and classification scheme for CNLs appears in Kuhn [Kuh14] The final output of NLG is clearly natural language of some kind.”