Using Correspondence Patterns to Identify Irregular Words in Cognate sets Through Leave-One-Out Validation

2026-02-02

AI Summary

This study addresses the lack of quantitative evaluation of phonological correspondence regularity in historical linguistic comparison, which hinders the effective identification of irregular word forms within cognate sets. The authors propose a novel regularity metric based on balanced mean recall and integrate it with leave-one-out validation and subsampling strategies to develop an automated method for detecting irregular forms. This work introduces balanced mean recall into computational historical linguistics for the first time, effectively capturing systematic deviations in sound correspondence patterns. Evaluated on real-world cognate datasets, the method achieves 85% accuracy in identifying irregular forms, substantially improving the data quality of cognate sets and providing a more reliable foundation for historical language reconstruction.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Machine Learning: Evaluation and Analysis
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Regular sound correspondences constitute the principal evidence in historical language comparison. Despite the heuristic focus on regularity, it is often more an intuitive judgement than a quantified evaluation, and irregularity is more common than expected from the Neogrammarian model. Given the recent progress of computational methods in historical linguistics and the increased availability of standardized lexical data, we are now able to improve our workflows and provide such a quantitative evaluation. Here, we present the balanced average recurrence of correspondence patterns as a new measure of regularity. We also present a new computational method that uses this measure to identify cognate sets that lack regularity with respect to their correspondence patterns. We validate the method through two experiments, using simulated and real data. In the experiments, we employ leave-one-out validation to measure the regularity of cognate sets in which one word form has been replaced by an irregular one, checking how well our method identifies the forms causing the irregularity. Our method achieves an overall accuracy of 85\% with the datasets based on real data. We also show the benefits of working with subsamples of large datasets and how increasing irregularity in the data influences our results. Reflecting on the broader potential of our new regularity measure and the irregular cognate identification method based on it, we conclude that they could play an important role in improving the quality of existing and future datasets in computer-assisted language comparison.

Problem

• regularity
• irregularity
• cognate sets
• sound correspondences
• historical linguistics

Innovation

• correspondence patterns
• regularity measure
• cognate identification
• leave-one-out validation
• computational historical linguistics