# Wiktionary as a Crowdsourced Lexicon for English Dialects

arXiv: https://arxiv.org/abs/2608.15641 · HTML: https://mlsanity.com/paper/2608.15641/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 60 references that are in the map.
- First posted on arXiv 16 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Geographic and Geopolitical Biases of Language Models](https://mlsanity.com/paper/2212.10408/) (2022)
- [Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks](https://mlsanity.com/paper/2410.11005/) (2024)
- [Do Large Language Models Understand Word Senses?](https://mlsanity.com/paper/2509.13905/) (2025)
- [Evaluation of LLMs in Medical Text Summarization: The Role of Vocabulary Adaptation in High OOV Settings](https://mlsanity.com/paper/2505.21242/) (2025)
- [Extracting Lexical Features from Dialects via Interpretable Dialect Classifiers](https://mlsanity.com/paper/2402.17914/) (2024)
- [Language, Place, and Social Media: Geographic Dialect Alignment in New Zealand](https://mlsanity.com/paper/2604.15744/) (2026)
- [Lexicons and grammars for language processing: industrial or handcrafted products?](https://mlsanity.com/paper/2606.03412/) (2026)

## Abstract (arXiv)

This paper evaluates Wiktionary as an ethically crowdsourced lexicon for English dialects. We took a two-phase approach, providing an in-depth descriptive analysis of the crowdsourced lexicon for 12 national varieties of English before applying the lexicon to geo-referenced, country-level social media language data to examine the real-world performance of this crowdsourced dialect lexicon. We demonstrate that Wiktionary matches or exceeds the coverage of traditional dictionaries, such as the Oxford English Dictionary (OED), for regional and Outer-Circle varieties. Our dialect-specific case study on New Zealand English found high alignment between Wiktionary and the OED based on word-formation patterns (R = 0.883). Similarly, we observed high alignment between the dialect lexicon and geo-referenced social media language. While this paper found that Wiktionary has broad coverage of lexical properties, it also highlighted some of the macro-challenges involved in evaluating dialect-responsive language resources and tools, such as the role of language contact in dialects and register effects in web-based corpora.