# Exploring the Constructicon: Linguistic Analysis of a Computational CxG

arXiv: https://arxiv.org/abs/2301.12642 · HTML: https://mlsanity.com/paper/2301.12642/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 30 Jan 2023.

## Builds on (most cited first, 14 of 14)

- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Compressive Transformers for Long-Range Sequence Modelling](https://mlsanity.com/paper/1911.05507/) (2019)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Mapping Languages: The Corpus of Global Language Use](https://mlsanity.com/paper/2004.00798/) (2020)
- [Modeling Global Syntactic Variation in English Using Dialect Classification](https://mlsanity.com/paper/1904.05527/) (2019)
- [Frequency vs. Association for Constraint Selection in Usage-Based Construction Grammar](https://mlsanity.com/paper/1904.05529/) (2019)
- [Finding Variants for Construction-Based Dialectometry: A Corpus-Based Approach to Regional CxGs](https://mlsanity.com/paper/2104.01299/) (2021)
- [Global Syntactic Variation in Seven Languages: Towards a Computational Dialectology](https://mlsanity.com/paper/2104.01306/) (2021)
- [A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging](https://mlsanity.com/paper/1412.4021/) (2014)
- [Modeling the Complexity and Descriptive Adequacy of Construction Grammars](https://mlsanity.com/paper/1904.05588/) (2019)
- [Production vs Perception: The Role of Individuality in Usage-Based Grammar Induction](https://mlsanity.com/paper/2104.09033/) (2021)
- [Learned Construction Grammars Converge Across Registers Given Increased Exposure](https://mlsanity.com/paper/2110.05663/) (2021)
- [Exposure and Emergence in Usage-Based Grammar: Computational Experiments in 35 Languages](https://mlsanity.com/paper/2211.14160/) (2022)
- [Stability of Syntactic Dialect Classification Over Space and Time](https://mlsanity.com/paper/2209.04958/) (2022)

## Built on by (most cited first, 3 of 3)

- [Enhancing Language Representation with Constructional Information for Natural Language Understanding](https://mlsanity.com/paper/2306.02819/) (2023)
- [Syntactic Variation Across the Grammar: Modelling a Complex Adaptive System](https://mlsanity.com/paper/2309.11869/) (2023)
- [The Computational Learning of Construction Grammars: State of the Art and Prospective Roadmap](https://mlsanity.com/paper/2407.07606/) (2024)

## Abstract (arXiv)

Recent work has formulated the task for computational construction grammar as producing a constructicon given a corpus of usage. Previous work has evaluated these unsupervised grammars using both internal metrics (for example, Minimum Description Length) and external metrics (for example, performance on a dialectology task). This paper instead takes a linguistic approach to evaluation, first learning a constructicon and then analyzing its contents from a linguistic perspective. This analysis shows that a learned constructicon can be divided into nine major types of constructions, of which Verbal and Nominal are the most common. The paper also shows that both the token and type frequency of constructions can be used to model variation across registers and dialects.