A Survey and Classification of Controlled Natural Languages (1507.01701v1)

Abstract: 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.

• The paper presents a detailed survey of over 100 English-based controlled natural languages developed since the 1930s to enhance both human and machine communication.
• It introduces the PENS framework—measuring Precision, Expressiveness, Naturalness, and Simplicity—to systematically classify CNLs based on their structural characteristics.
• The findings highlight a trade-off between expressiveness and simplicity, suggesting that increasing clarity in one dimension may necessitate compromises in another.

An Analysis of Controlled Natural Languages: Survey and Classification

Controlled Natural Languages (CNLs), as analyzed in Tobias Kuhn's extensive paper, are constructed languages tailored to improve human and machine communication through precise, restricted language frameworks. These languages represent a fascinating intersection of natural languages and formal systems, positioning themselves uniquely to enhance semantic clarity while still maintaining intuitive understanding for native speakers.

The paper offers a comprehensive examination of over 100 English-based CNLs developed since the 1930s. CNLs serve diverse purposes, primarily enhancing human comprehension, facilitating translation, and providing intuitive, human-readable representations of formal notations. Despite their varied applications, these languages are unified under a common taxonomy presented in the paper, offering insights into their structural characteristics and applicability across different domains.

Key Contributions

Kuhn categorizes CNLs within a conceptual space bridged by natural language on one side and formal logic on the other. This space is delineated using the PENS framework, which consists of four dimensions: Precision, Expressiveness, Naturalness, and Simplicity. This classification is instrumental in differentiating CNLs based on their intrinsic properties rather than merely their application scenarios.

1. Precision: The language's ability to convey meaning unambiguously from its textual form. This dimension is pivotal in evaluating CNLs' potential to mitigate contextual ambiguity, a persistent challenge in natural language processing.
2. Expressiveness: The extent to which a CNL can articulate various propositions. This examines the language's functional breadth, particularly its capability to accommodate complex logical structures.
3. Naturalness: How closely CNLs resemble natural language in syntax and semantics, retaining readability and intuitive comprehension for native speakers.
4. Simplicity: Measured by the complexity involved in precisely defining the language's complete syntax and semantics. This reflects the ease with which a language can be implemented and comprehended.

Observed Trends and Variability

The results reveal significant variation among existing CNLs across the PENS dimensions. Many languages focus heavily on either improving human comprehension (primarily originating from industry) or mapping to formal systems for automated reasoning (predominantly from academia). There is a clear trend towards increasing language precision and simplicity as primary objectives, especially in formal applications.

Interestingly, the analysis suggests a robust correlation between CNL precision and simplicity, indicating that more precise languages tend to require simpler structural definitions. Conversely, there is an inherent trade-off between expressiveness and simplicity, as more expressive languages typically demand greater complexity.

Practical Implications and Future Prospects

The survey illustrates the extensive potential and variety of CNL applications in domains such as software engineering, formal logic representation, aviation communication, and machine translation. The diversity in CNL frameworks enables tailored solutions that meet specific requirements in these fields, driving improvements in clarity and efficacy.

The paper implies that future CNL developments could significantly benefit from adopting insights and best practices observed in historically successful CNL systems. Continual advancements in natural language processing and formal reasoning systems will likely provide new opportunities for refining CNL frameworks, enhancing their utility in increasingly complex domains.

Kuhn's work serves as a vital repository for CNL researchers and practitioners, offering foundational insights into language construction and application. As technology demands more sophisticated human-computer interaction paradigms, CNLs are poised to play an integral role in shaping accessible, precise, and effective communication systems.