# Stability of Syntactic Dialect Classification Over Space and Time

arXiv: https://arxiv.org/abs/2209.04958 · HTML: https://mlsanity.com/paper/2209.04958/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 55 references that are in the map.
- First posted on arXiv 11 Sep 2022.

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

- [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)
- [CxGBERT: BERT meets Construction Grammar](https://mlsanity.com/paper/2011.04134/) (2020)
- [Characterizing English Variation across Social Media Communities with BERT](https://mlsanity.com/paper/2102.06820/) (2021)
- [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)
- [Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks](https://mlsanity.com/paper/1708.04358/) (2017)
- [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)
- [Point-of-Interest Type Inference from Social Media Text](https://mlsanity.com/paper/2009.14734/) (2020)

## Built on by (most cited first, 5 of 5)

- [Exposure and Emergence in Usage-Based Grammar: Computational Experiments in 35 Languages](https://mlsanity.com/paper/2211.14160/) (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)
- [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)

## Abstract (arXiv)

This paper analyses the degree to which dialect classifiers based on syntactic representations remain stable over space and time. While previous work has shown that the combination of grammar induction and geospatial text classification produces robust dialect models, we do not know what influence both changing grammars and changing populations have on dialect models. This paper constructs a test set for 12 dialects of English that spans three years at monthly intervals with a fixed spatial distribution across 1,120 cities. Syntactic representations are formulated within the usage-based Construction Grammar paradigm (CxG). The decay rate of classification performance for each dialect over time allows us to identify regions undergoing syntactic change. And the distribution of classification accuracy within dialect regions allows us to identify the degree to which the grammar of a dialect is internally heterogeneous. The main contribution of this paper is to show that a rigorous evaluation of dialect classification models can be used to find both variation over space and change over time.