用图结构追踪词语语义演变，无需预设词义库。
Word-Centered Semantic Graphs for Interpretable Diachronic Sense Tracking
- 以目标词为中心构建语义图，融合词向量与掩码语言模型。
- 通过聚类和节点重叠识别语义变化，发现特朗普、上帝等词的演变模式。
- 可视化语义动态，适合研究历史文本语义演化的人文计算学者。
我们提出一种可解释的图结构框架，用于分析历时语料中的语义演变。针对每个目标词和时间切片，构建以该词为中心的语义网络，结合历时跳字模型（diachronic Skip-gram）的分布相似性与特定时间段掩码语言模型（masked language models）的词汇替代性。通过聚类外围节点识别语义结构，利用节点重叠对齐不同时段的聚类，并通过聚类组成与归一化质量追踪语义变化。在1980–2017年《纽约时报杂志》语料的应用研究中，发现图连通性反映多义性动态：事件驱动的语义更替（如trump）、语义稳定但聚类过分割（god），以及与数字通信相关的渐进关联转移（post）。总体而言，词中心语义图提供了一种紧凑且透明的表示方式，无需依赖预定义词义库即可探索语义演化。
原文摘要 · Abstract (English)
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.
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