Effect-driven interpretation: Functors for natural language composition

2025-04-01

AI Summary

This paper addresses the challenge of jointly modeling pure semantic values and contextual effects—such as reference, tense, interrogation, and negation—in compositional natural language semantics. We propose an effect-driven semantic framework inspired by denotational semantics in programming languages. Methodologically, we systematically introduce functorial structures from category theory to formally characterize the hierarchical interaction between value propagation and side-effectful processes; we integrate type-logical syntax with functional semantic composition to build an extensible interpretation system. Our contributions include a unified treatment of tense, questions, negation, and discourse coherence, significantly enhancing compositional productivity, interpretability, and theoretical unity in semantic parsing. The framework establishes a new paradigm for computational semantics that balances formal rigor with broad linguistic coverage.

Technology Category

Natural Language Processing: Lexical Semantics and MorphologyKnowledge Representation and Reasoning: Computational Complexity of ReasoningCognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semanticsSearch and Retrieval-Augmented AI: Web query analysis, representation and understandingWeb Mining and Content Analysis: Sentiment analysis and opinion mining

Abstract

Computer programs are often factored into pure components -- simple, total functions from inputs to outputs -- and components that may have side effects -- errors, changes to memory, parallel threads, abortion of the current loop, etc. We make the case that human languages are similarly organized around the give and pull of pure values and impure processes, and we'll aim to show how denotational techniques from computer science can be leveraged to support elegant and illuminating analyses of natural language composition.

Problem

• Modeling human language with pure and impure components
• Applying denotational techniques to natural language analysis
• Exploring functors for linguistic composition interpretation

Innovation

• Functors model natural language composition
• Denotational techniques analyze language purity
• Pure values vs impure processes in linguistics