Abstract
The meanings and relationships of words shift over time. This phenomenon is referred to as semantic this http URL focused on understanding how semantic shifts occur over multiple time periods is essential for gaining a detailed understanding of semantic this http URL, 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 this http URL 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 this http URL compute a diachronic word similarity matrix using fast and lightweight word embeddings across arbitrary time periods, making it deeper to analyze continuous semantic this http URL, by clustering the similarity matrices for different words, we can categorize words that exhibit similar behavior of semantic shift in an unsupervised manner.
Abstract (translated)
单词的意义和关系会随着时间的推移而发生变化，这种现象被称为语义漂移。为了深入了解这种多时期内发生的语义变化，研究在不同时间间隔期间检测到的变化点是不够的，并且使用基于BERT的方法来考察词义比例也面临着较高的计算成本问题。 为了解决这些问题，我们提出了一种简单直观的框架，通过利用相同词汇在其嵌入表示之间的相似性矩阵，以描述多时期内语义变化的发生。我们的方法可以跨任意时间间隔计算出一种历时性的单词相似度矩阵，并使用快速且轻量级的词向量来实现这一点，从而更深入地分析连续的语义变化。 通过聚类不同词汇的相似度矩阵，我们可以无监督地将表现出类似语义漂移行为的词语归为一类。
URL
https://arxiv.org/abs/2501.09538
