languagecan

LanguageCan is a theoretical framework and domain-specific language intended to encode and reason about linguistic capabilities of natural languages within artificial intelligence systems. It aims to provide a structured, machine readable representation of how language conveys modality, possibility, obligation, and related cognitive aspects, enabling researchers to compare and integrate linguistic resources across languages and models. The term LanguageCan is sometimes used as an acronym, though usage varies.

Developed in speculative discussions within computational linguistics and AI research communities, LanguageCan is not a widely adopted standard. It remains a conceptual proposal with few, if any, broadly recognized implementations.

Core design emphasizes modular annotation layers for lexical semantics, syntax, discourse, and pragmatic context. It supports encoding of modality (can, may, must), negation, tense, and aspect, along with cross-linguistic correspondences and links to external ontologies and corpora. The syntax is intended to be human readable and machine parseable, with serialization options such as JSON-LD and YAML to facilitate interoperability.

Applications proposed for LanguageCan include organizing and evaluating multilingual language resources, benchmarking language models on cross-linguistic tasks, and providing a formal substrate for integrating corpora with language-agnostic evaluation metrics. By enabling consistent annotation across languages, LanguageCan seeks to reduce ad hoc resource creation and improve cross-lingual analysis.

Status and outlook: The concept remains niche and experimental, with critique centered on fragmentation risk and the lack of official endorsement. If developed further, LanguageCan could complement existing standards for lexical and syntactic annotation, but its long-term viability will depend on community consensus and tool support.