# Global Syntactic Variation in Seven Languages: Towards a Computational Dialectology

arXiv: https://arxiv.org/abs/2104.01306 · HTML: https://mlsanity.com/paper/2104.01306/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 83 references that are in the map.
- First posted on arXiv 3 Apr 2021.

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

- [Dialectometric analysis of language variation in Twitter](https://mlsanity.com/paper/1702.06777/) (2017)
- [Modeling Global Syntactic Variation in English Using Dialect Classification](https://mlsanity.com/paper/1904.05527/) (2019)
- [Where in the World are You? Geolocation and Language Identification in Twitter](https://mlsanity.com/paper/1308.0683/) (2013)
- [Frequency vs. Association for Constraint Selection in Usage-Based Construction Grammar](https://mlsanity.com/paper/1904.05529/) (2019)
- [Modeling the Complexity and Descriptive Adequacy of Construction Grammars](https://mlsanity.com/paper/1904.05588/) (2019)
- [Mapping Languages and Demographics with Georeferenced Corpora](https://mlsanity.com/paper/2004.00809/) (2020)
- [Using Robust PCA to estimate regional characteristics of language use from geo-tagged Twitter messages](https://mlsanity.com/paper/1311.1169/) (2013)

## Built on by (most cited first, 11 of 11)

- [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)
- [Corpus Similarity Measures Remain Robust Across Diverse Languages](https://mlsanity.com/paper/2206.04332/) (2022)
- [Exploring the Constructicon: Linguistic Analysis of a Computational CxG](https://mlsanity.com/paper/2301.12642/) (2023)
- [Variation and Instability in Dialect-Based Embedding Spaces](https://mlsanity.com/paper/2303.14963/) (2023)
- [Syntactic Variation Across the Grammar: Modelling a Complex Adaptive System](https://mlsanity.com/paper/2309.11869/) (2023)
- [Exploring language relations through syntactic distances and geographic proximity](https://mlsanity.com/paper/2403.18430/) (2024)
- [Validating and Exploring Large Geographic Corpora](https://mlsanity.com/paper/2403.08198/) (2024)
- [Detecting Linguistic Diversity on Social Media](https://mlsanity.com/paper/2502.21224/) (2025)

## Abstract (arXiv)

The goal of this paper is to provide a complete representation of regional linguistic variation on a global scale. To this end, the paper focuses on removing three constraints that have previously limited work within dialectology/dialectometry. First, rather than assuming a fixed and incomplete set of variants, we use Computational Construction Grammar to provide a replicable and falsifiable set of syntactic features. Second, rather than assuming a specific area of interest, we use global language mapping based on web-crawled and social media datasets to determine the selection of national varieties. Third, rather than looking at a single language in isolation, we model seven major languages together using the same methods: Arabic, English, French, German, Portuguese, Russian, and Spanish. Results show that models for each language are able to robustly predict the region-of-origin of held-out samples better using Construction Grammars than using simpler syntactic features. These global-scale experiments are used to argue that new methods in computational sociolinguistics are able to provide more generalized models of regional variation that are essential for understanding language variation and change at scale.