A Method for Learning Large-Scale Computational Construction Grammars from Semantically Annotated Corpora

2026-03-13

AI Summary

This work proposes a computational approach to automatically induce a broad-coverage, interpretable construction grammar from large-scale, jointly annotated syntactic and semantic corpora, modeling the complex mapping between syntactic structures and semantic relations. Leveraging the Fluid Construction Grammar framework, we present the first large-scale automatic induction of constructions, resulting in a network comprising tens of thousands of grammatical constructions. The system not only demonstrates the scalability of construction grammar theory under big-data conditions but also provides an efficient and interpretable tool for open-domain semantic parsing and the study of English argument structure. Furthermore, it reveals rich usage patterns linking syntax and semantics embedded in natural language data.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Knowledge Representation and Reasoning: Argumentation
• Cognitive Modeling & Cognitive Systems: Computational Creativity

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 semantics
• Web Mining and Content Analysis: Large pretrained models with web data
• Social Networks and Social Media: Computational social science

Abstract

We present a method for learning large-scale, broad-coverage construction grammars from corpora of language use. Starting from utterances annotated with constituency structure and semantic frames, the method facilitates the learning of human-interpretable computational construction grammars that capture the intricate relationship between syntactic structures and the semantic relations they express. The resulting grammars consist of networks of tens of thousands of constructions formalised within the Fluid Construction Grammar framework. Not only do these grammars support the frame-semantic analysis of open-domain text, they also house a trove of information about the syntactico-semantic usage patterns present in the data they were learnt from. The method and learnt grammars contribute to the scaling of usage-based, constructionist approaches to language, as they corroborate the scalability of a number of fundamental construction grammar conjectures while also providing a practical instrument for the constructionist study of English argument structure in broad-coverage corpora.

Problem

• construction grammar
• semantic annotation
• large-scale grammar learning
• syntax-semantics interface
• corpus-based learning

Innovation

• computational construction grammar
• frame semantics
• grammar induction
• Fluid Construction Grammar
• usage-based linguistics