semanticus

Semanticus is a theoretical framework used in linguistics and artificial intelligence to model meaning across language and modalities. It posits layered semantic content where lexical items map to compact representations called semantons, which can be combined to form larger meanings. Semantons aim to capture referential content and pragmatic force, allowing context-sensitive interpretation.

In semanticus meanings are not fixed labels but vectors or symbolic structures that interact with context, world knowledge, and user intent. The framework emphasizes compositionality: the meaning of a sentence comes from its parts and the rules that combine them. To handle polysemy and cross-linguistic variation, semanticus uses alignment procedures that map semantons across languages and domains.

Methodologically, semanticus supports a formal calculus for combining semantons and for how contextual operators modify them. It is designed to be compatible with statistical and neural methods, providing an interpretable layer atop distributional representations.

Applications include natural language understanding, knowledge graph population, ontology alignment, cross-lingual retrieval, and cognitive modeling. In philosophy of language, semanticus contributes to debates on meaning, reference, and context-dependence.

Critics point to complexity, data demands, and validation challenges. Proponents argue that the framework offers a useful bridge between symbolic and probabilistic approaches and can improve interpretability of semantic models. See also: semantics, pragmatics, compositionality, knowledge representation, ontology.