Dynamics of meaning: Towards the Evaluation of Diachronic Semantic Change in Sinhala

2026-09-08

AI Summary

本文研究僧伽罗语从13世纪到20世纪的语义演变，通过多阶段计算框架和多种对齐技术解决低资源语言数据稀缺问题。

Abstract

Tracking semantic change in low-resource languages across extensive historical timelines presents significant challenges due to data scarcity and the limitations of static embedding alignments. This study investigates the diachronic evolution of the Sinhala language from the 13th to the 20th century using a multi-stage computational framework. We first align century-specific Word2Vec and FastText embeddings using Similarity Matrix Based Alignment (SMA) and Orthogonal Procrustes (OP) techniques, finding that OP alignment provides more stable neighbourhood tracking for identifying temporal similarity dips. To move beyond aggregate measures, we introduce a Bidirectional Semantic Impact Pruning approach using contextualised embeddings from a fine-tuned Llama-3.1-8B. By applying Leave-One-Out (LOO) diagnostics, we attempt to isolate influential sentences to distinguish between systemic semantic shifts and transient polysemic expansion. Our results show that semantic drift in the fine-tuned Llama-3.1-8B is not evenly distributed across all usages. Instead, a significant part of the change is driven by a smaller set of high-impact contextual instances, rather than gradual and uniform change across all occurrences. This work provides a preliminary framework for diachronic analysis in low-resource contexts, highlighting the trade-offs between model sensitivity and data availability.

Problem

• semantic change
• low-resource languages
• diachronic evolution
• data scarcity
• embedding alignment

Innovation

• Orthogonal Procrustes (OP)
• Bidirectional Semantic Impact Pruning
• contextualised embeddings
• Llama-3.1-8B
• diachronic semantic change