Year: 2025
Type: preprint
Abstract: This paper presents a fully self-supervised approach to borrowing detection in multilingual wordlists. The method combines two sources of information: PMI similarities based on a global correspondence model and a lightweight contrastive component trained on phonetic feature vectors. It further includes an automatic procedure for selecting decision thresholds without requiring labeled data. Experiments on benchmark datasets show that PMI alone already improves over existing string similarity measures such as NED and SCA, and that the combined similarity performs on par with or better than supervised baselines. An ablation study highlights the importance of character encoding, temperature settings and augmentation strategies. The approach scales to datasets of different sizes, works without manual supervision and is provided with a command-line tool that allows researchers to conduct their own studies.
Source: arXiv (Cornell University)
Author: Wientzek, Tim
Institution: University of Tübingen
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Deutsche Forschungsgemeinschaft
Awards: G534521444, G7258815532
OpenAlex ID: W4416966519