用词向量相似矩阵分析词语意义随时间的连续变化。
Analyzing Continuous Semantic Shifts with Diachronic Word Similarity Matrices
- 构建跨时段词向量相似矩阵，捕捉语义演变过程
- 通过轻量级嵌入实现任意时段的快速计算
- 无监督聚类揭示具有相似演变模式的词汇群
词语的意义和关系会随时间发生变化，这一现象称为语义漂移。研究多时段语义漂移的演变机制对于深入理解语义变化至关重要。然而，仅在相邻时段间检测变化点难以揭示细致的语义演变过程；而使用BERT方法分析词义分布则计算成本过高。为此，我们提出一种简单直观的框架，通过分析同一词语在不同时段的嵌入表示之间的相似性矩阵，来刻画语义漂移的连续过程。利用快速轻量的词嵌入，在任意时间区间上构建历时性词相似矩阵，提升分析深度。此外，通过对不同词语的相似矩阵进行聚类，可无监督地将表现出相似语义漂移行为的词汇归为一类。
原文摘要 · Abstract (English)
The meanings and relationships of words shift over time. This phenomenon is referred to as semantic shift. Research focused on understanding how semantic shifts occur over multiple time periods is essential for gaining a detailed understanding of semantic shifts. However, detecting change points only between adjacent time periods is insufficient for analyzing detailed semantic shifts, and using BERT-based methods to examine word sense proportions incurs a high computational cost. To address those issues, we propose a simple yet intuitive framework for how semantic shifts occur over multiple time periods by leveraging a similarity matrix between the embeddings of the same word through time. We compute a diachronic word similarity matrix using fast and lightweight word embeddings across arbitrary time periods, making it deeper to analyze continuous semantic shifts. Additionally, by clustering the similarity matrices for different words, we can categorize words that exhibit similar behavior of semantic shift in an unsupervised manner.
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