Word-Centered Semantic Graphs for Interpretable Diachronic Sense Tracking

TL;DR

This paper introduces an interpretable graph-based method for tracking semantic shifts of words over time using diachronic corpora, combining distributional and lexical information to analyze sense evolution.

Contribution

It presents a novel word-centered semantic graph framework that captures sense changes without predefined sense inventories, integrating multiple linguistic signals for diachronic analysis.

Findings

Graph connectivity reflects polysemy dynamics.

Communities capture contrasting sense trajectories.

Method reveals sense evolution in real-world corpus.

Abstract

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.