contextussanasta

Contextussanasta is a theoretical framework within linguistics and digital humanities that examines how contextual factors influence linguistic meaning and interpretation across texts and discourse. It treats context as an active, dynamic component rather than a static backdrop.

The approach defines three hierarchical context levels: micro-context (the local sentence and immediate co-text), meso-context (paragraphs, discourse segments, and genre conventions), and macro-context (cultures, communities, and historical periods). It uses networks to model how words, phrases, and discourse moves relate to each other within these contexts, capturing how meaning shifts with changing environmental cues.

Methodologically, researchers segment corpora, annotate contextual factors such as speaker role, genre, and discourse function, and construct contextual networks. Quantitative metrics include contextual entropy, co-context frequency, and salience scores; qualitative analyses examine interpretive flexibility, shifts in meaning, and the influence of discourse moves on interpretation.

Applications span authorship attribution, historical linguistics, sentiment and stance analysis, moderation and content analysis, and explainable artificial intelligence in natural language processing. The framework emphasizes interpretability by linking computational outputs to human-understandable contexts and discursive dynamics.

Relation to other work positions contextussanasta as complementary to distributional semantics and contextualized language models. While those approaches focus on representations of words or embeddings, contextussanasta emphasizes context as a relational and discursive structure that shapes meaning across levels of discourse. It draws on theories from cognitive semantics, pragmatics, and discourse analysis to inform both methodological practice and interpretive analysis.

Limitations include computational complexity, data sparsity for rare contexts, and potential labeling bias in context annotation. As a developing framework, contextussanasta continues to refine its boundaries, metrics, and best practices for robust, transparent analysis of context-driven meaning.