# Representing and Computing Uncertainty in Phonological Reconstruction

arXiv: https://arxiv.org/abs/2310.12727 · HTML: https://mlsanity.com/paper/2310.12727/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Ab Antiquo: Neural Proto-language Reconstruction](https://mlsanity.com/paper/1908.02477/) (2019)
- [A New Framework for Fast Automated Phonological Reconstruction Using Trimmed Alignments and Sound Correspondence Patterns](https://mlsanity.com/paper/2204.04619/) (2022)
- [Trimming Phonetic Alignments Improves the Inference of Sound Correspondence Patterns from Multilingual Wordlists](https://mlsanity.com/paper/2303.17932/) (2023)

## Built on by (most cited first, 1 of 1)

- [Using Correspondence Patterns to Identify Irregular Words in Cognate sets Through Leave-One-Out Validation](https://mlsanity.com/paper/2602.02221/) (2026)

## Abstract (arXiv)

Despite the inherently fuzzy nature of reconstructions in historical linguistics, most scholars do not represent their uncertainty when proposing proto-forms. With the increasing success of recently proposed approaches to automating certain aspects of the traditional comparative method, the formal representation of proto-forms has also improved. This formalization makes it possible to address both the representation and the computation of uncertainty. Building on recent advances in supervised phonological reconstruction, during which an algorithm learns how to reconstruct words in a given proto-language relying on previously annotated data, and inspired by improved methods for automated word prediction from cognate sets, we present a new framework that allows for the representation of uncertainty in linguistic reconstruction and also includes a workflow for the computation of fuzzy reconstructions from linguistic data.