# Dynamics of Meaning: Towards the Evaluation of Diachronic Semantic Change in Sinhala

arXiv: https://arxiv.org/abs/2609.08609 · HTML: https://mlsanity.com/paper/2609.08609/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 154 references that are in the map.
- First posted on arXiv 8 Sep 2026.

## Builds on (most cited first, 30 of 32)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection](https://mlsanity.com/paper/2007.11464/) (2020)
- [Analysing Lexical Semantic Change with Contextualised Word Representations](https://mlsanity.com/paper/2004.14118/) (2020)
- [Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change](https://mlsanity.com/paper/1606.02821/) (2016)
- [Survey of Computational Approaches to Lexical Semantic Change](https://mlsanity.com/paper/1811.06278/) (2018)
- [Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP World](https://mlsanity.com/paper/2210.08523/) (2022)
- [Temporal Analysis of Language through Neural Language Models](https://mlsanity.com/paper/1405.3515/) (2014)
- [Dynamic Word Embeddings](https://mlsanity.com/paper/1702.08359/) (2017)
- [On the Dimensionality of Word Embedding](https://mlsanity.com/paper/1812.04224/) (2018)
- [Survey on Publicly Available Sinhala Natural Language Processing Tools and Research](https://mlsanity.com/paper/1906.02358/) (2019)
- [Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift](https://mlsanity.com/paper/1912.01072/) (2019)
- [Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change](https://mlsanity.com/paper/1804.06517/) (2018)
- [A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change](https://mlsanity.com/paper/2402.12011/) (2024)
- [RuSemShift: a dataset of historical lexical semantic change in Russian](https://mlsanity.com/paper/2010.06436/) (2020)
- [Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang](https://mlsanity.com/paper/2203.04651/) (2022)
- [High-risk learning: acquiring new word vectors from tiny data](https://mlsanity.com/paper/1707.06556/) (2017)
- [Semantic Drift in Multilingual Representations](https://mlsanity.com/paper/1904.10820/) (2019)
- [Three-part diachronic semantic change dataset for Russian](https://mlsanity.com/paper/2106.08294/) (2021)
- [CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations](https://mlsanity.com/paper/2005.06602/) (2020)
- [Zero-shot OCR Accuracy of Low-Resourced Languages: A Comparative Analysis on Sinhala and Tamil](https://mlsanity.com/paper/2507.18264/) (2025)
- [SinLlama -- A Large Language Model for Sinhala](https://mlsanity.com/paper/2508.09115/) (2025)
- [ELMo and BERT in semantic change detection for Russian](https://mlsanity.com/paper/2010.03481/) (2020)
- [UWB at SemEval-2020 Task 1: Lexical Semantic Change Detection](https://mlsanity.com/paper/2012.00004/) (2020)
- [Dynamic Bernoulli Embeddings for Language Evolution](https://mlsanity.com/paper/1703.08052/) (2017)
- [Lexical semantic change for Ancient Greek and Latin](https://mlsanity.com/paper/2101.09069/) (2021)
- [An open diachronic corpus of historical Spanish: annotation criteria and automatic modernisation of spelling](https://mlsanity.com/paper/1306.3692/) (2013)
- [Empirical Study of Diachronic Word Embeddings for Scarce Data](https://mlsanity.com/paper/1909.01863/) (2019)
- [Invariance and identifiability issues for word embeddings](https://mlsanity.com/paper/1911.02656/) (2019)

## Abstract (arXiv)

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 low-resource Sinhala diachronic analysis, highlighting the trade-offs between model sensitivity and data availability.