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

Paul Van Eecke, Katrien Beuls

Keywords: Construction Grammar, Usage-based Linguistics, Meaning Representation, Fluid Construction Grammar, Usage-based Grammar Learning

TL;DR: We present a method for learning large-scale, broad-coverage, usage-based construction grammars from semantically annotated corpora.

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.

Scope Confirmation: To the best of my judgment, this submission falls within the scope of CoNLL.

Primary Area Selection: Computational Usage-Based Grammars (e.g., Construction Grammars)

Use Of Generative Artificial Intelligence Tools: No, not at all

Data Collection From Human Subjects: No

Submission Type: Archival: I certify that the submission has not been previously published, nor is the material in it under review by another journal or conference. Further, no material in it will be submitted for review at another conference or journal while under review by CoNLL 2026.

Submission Number: 45