# Modeling Global Syntactic Variation in English Using Dialect Classification

arXiv: https://arxiv.org/abs/1904.05527 · HTML: https://mlsanity.com/paper/1904.05527/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2019.

## Built on by (most cited first, 13 of 13)

- [Natural Language Processing for Dialects of a Language: A Survey](https://mlsanity.com/paper/2401.05632/) (2024)
- [Geographic Adaptation of Pretrained Language Models](https://mlsanity.com/paper/2203.08565/) (2022)
- [Global Syntactic Variation in Seven Languages: Towards a Computational Dialectology](https://mlsanity.com/paper/2104.01306/) (2021)
- [Representations of Language Varieties Are Reliable Given Corpus Similarity Measures](https://mlsanity.com/paper/2104.01294/) (2021)
- [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)
- [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)
- [Measuring Linguistic Diversity During COVID-19](https://mlsanity.com/paper/2104.01290/) (2021)
- [Validating and Exploring Large Geographic Corpora](https://mlsanity.com/paper/2403.08198/) (2024)

## Abstract (arXiv)

This paper evaluates global-scale dialect identification for 14 national varieties of English as a means for studying syntactic variation. The paper makes three main contributions: (i) introducing data-driven language mapping as a method for selecting the inventory of national varieties to include in the task; (ii) producing a large and dynamic set of syntactic features using grammar induction rather than focusing on a few hand-selected features such as function words; and (iii) comparing models across both web corpora and social media corpora in order to measure the robustness of syntactic variation across registers.