SCOPE-Fact: Linguistic Scope-Guided Composition of Anchored Stochastic Operators for Predicting Event Factuality in Natural Language

Event factuality prediction, a task in natural language processing, estimates the commitment conveyed by a text toward an event. Nested negation and modality require inference sensitive to the linguistic scope, but endpoint labels can leave intermediate transformations underdetermined. We introduce SCOPE-Fact, which learns five-state stochastic operators from isolated lexical and grammatical signatures and composes them in scope order. Parent–child commitment judgments constrain local transformations, while a log-determinant acquisition criterion allocates a fixed annotation budget. Unsupported inputs use an independent text classifier shared across mechanistic comparisons. Our analysis establishes conditional recovery and perturbation bounds, gives a necessary total-variation diagnostic for local representation adequacy, and characterizes observable order effects through commutators. With five ratings per proposition, the simultaneous adequacy bound is inconclusive at the population level. The reported empirical scores are 51.5% macro-F1 on MAVEN-FACT public development and 69.0%, 59.0%, and 48.0% pair accuracy under combination, order, and depth holdouts. Relative to an equally supervised nonlinear recurrence, the point differences are 51.5-51.0, 69.0-65.0, 59.0-55.0, and 48.0-43.0 percentage points. The combination-holdout difference from the no-anchor ablation is 69.0-58.0 points. These descriptive comparisons are interpreted jointly with routing coverage and paired uncertainty. The framework makes the predictive reuse of local commitment transformations testable within a defined linguistic core, while the mathematical analysis separates prediction from population identifiability and representation adequacy.