利用历时词相似矩阵分析连续语义偏移
Analyzing Continuous Semantic Shifts with Diachronic Word Similarity Matrices
AI总结：
针对相邻时间段变化点检测不足与BERT类方法计算成本高的问题，本研究提出基于历时词相似矩阵的轻量框架，可深入分析多时段连续语义偏移，并能无监督聚类语义偏移行为相似的词语。
摘要：
词语的含义与关联会随时间发生变化，这一现象被称为语义偏移。聚焦于理解多个时间段内语义偏移如何发生的研究，对于深入掌握语义偏移的细节至关重要。然而，仅检测相邻时间段之间的变化点不足以分析精细的语义偏移，而使用基于BERT的方法考察词义占比又会产生极高的计算成本。为解决这些问题，我们提出了一个简单且直观的框架，通过利用同一词语跨时间的嵌入向量之间的相似矩阵，揭示多个时间段内语义偏移的发生机制。我们使用快速轻量的词嵌入计算任意时间段内的历时词相似矩阵，使连续语义偏移的分析更为深入。此外，通过对不同词语的相似矩阵进行聚类，我们能够以无监督方式对呈现相似语义偏移行为的词语进行分类。
英文摘要：
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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