# Word-Centered Semantic Graphs for Interpretable Diachronic Sense Tracking

arXiv: https://arxiv.org/abs/2601.22410 · HTML: https://mlsanity.com/paper/2601.22410/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 29 Jan 2026.

## Builds on (most cited first, 12 of 12)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [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)
- [Diachronic word embeddings and semantic shifts: a survey](https://mlsanity.com/paper/1806.03537/) (2018)
- [Analysing Lexical Semantic Change with Contextualised Word Representations](https://mlsanity.com/paper/2004.14118/) (2020)
- [Graph-based Clustering for Detecting Semantic Change Across Time and Languages](https://mlsanity.com/paper/2402.01025/) (2024)
- [SST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in BERT-based Embedding Spaces](https://mlsanity.com/paper/2010.00857/) (2020)
- [Zeitenwenden: Detecting changes in the German political discourse](https://mlsanity.com/paper/2410.17960/) (2024)
- [Topological properties and organizing principles of semantic networks](https://mlsanity.com/paper/2304.12940/) (2023)
- [Identifying economic narratives in large text corpora -- An integrated approach using Large Language Models](https://mlsanity.com/paper/2506.15041/) (2025)
- [Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models](https://mlsanity.com/paper/2506.20269/) (2025)

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

- [Coupled Usage-Sense Processes: Temporal and Attributable Lexical Semantic Change](https://mlsanity.com/paper/2609.30974/) (2026)

## Abstract (arXiv)

We propose an interpretable, graph-based framework for analyzing semantic shift in diachronic corpora. For each target word and time slice, we induce a word-centered semantic network that integrates distributional similarity from diachronic Skip-gram embeddings with lexical substitutability from time-specific masked language models. We identify sense-related structure by clustering the peripheral graph, align clusters across time via node overlap, and track change through cluster composition and normalized cluster mass. In an application study on a corpus of New York Times Magazine articles (1980 - 2017), we show that graph connectivity reflects polysemy dynamics and that the induced communities capture contrasting trajectories: event-driven sense replacement (trump), semantic stability with cluster over-segmentation effects (god), and gradual association shifts tied to digital communication (post). Overall, word-centered semantic graphs offer a compact and transparent representation for exploring sense evolution without relying on predefined sense inventories.