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

arXiv: https://arxiv.org/abs/2603.12754 · HTML: https://mlsanity.com/paper/2603.12754/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 79 references that are in the map.
- First posted on arXiv 13 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [Constituency Parsing with a Self-Attentive Encoder](https://mlsanity.com/paper/1805.01052/) (2018)
- [The Better Your Syntax, the Better Your Semantics? Probing Pretrained Language Models for the English Comparative Correlative](https://mlsanity.com/paper/2210.13181/) (2022)
- [CxGBERT: BERT meets Construction Grammar](https://mlsanity.com/paper/2011.04134/) (2020)
- [UCxn: Typologically Informed Annotation of Constructions Atop Universal Dependencies](https://mlsanity.com/paper/2403.17748/) (2024)
- [Exposure and Emergence in Usage-Based Grammar: Computational Experiments in 35 Languages](https://mlsanity.com/paper/2211.14160/) (2022)
- [The Computational Learning of Construction Grammars: State of the Art and Prospective Roadmap](https://mlsanity.com/paper/2407.07606/) (2024)
- [PyFCG: Fluid Construction Grammar in Python](https://mlsanity.com/paper/2505.12920/) (2025)

## Built on by (most cited first, 1 of 1)

- [Evolving language compositionality in a frequency-structured meaning space](https://mlsanity.com/paper/2607.29642/) (2026)

## Abstract (arXiv)

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.