用不平衡最优传输量化词语用法层面的语义变迁
Quantifying Lexical Semantic Shift via Unbalanced Optimal Transport
- 引入不平衡最优传输分析上下文词向量，捕捉用法实例间的语义变化
- 提出SUS度量，可精准追踪每个用法实例中词义频率的增减变化
- 统一解决实例级语义变化、词义广化/窄化等核心难题，适合语言演变研究者
词汇语义变迁检测旨在识别词语意义随时间的变化。现有基于历时语料库对的嵌入方法虽能估算目标词的变迁程度，却难以提供个体用法实例层面的洞察。为此，本文将不平衡最优传输（UOT）应用于上下文词向量集合，通过用法实例间对齐的过剩与缺失来捕捉语义变迁。特别地，我们提出一种名为“词义用法变迁”（Sense Usage Shift, SUS）的度量，用于量化每个用法实例中词义使用频率的变化。借助SUS，我们可统一处理多个语义变迁检测挑战，包括实例级语义变化的量化，以及词级任务如语义变迁幅度、词义广化或窄化的测量。
原文摘要 · Abstract (English)
Lexical semantic change detection aims to identify shifts in word meanings over time. While existing methods using embeddings from a diachronic corpus pair estimate the degree of change for target words, they offer limited insight into changes at the level of individual usage instances. To address this, we apply Unbalanced Optimal Transport (UOT) to sets of contextualized word embeddings, capturing semantic change through the excess and deficit in the alignment between usage instances. In particular, we propose Sense Usage Shift (SUS), a measure that quantifies changes in the usage frequency of a word sense at each usage instance. By leveraging SUS, we demonstrate that several challenges in semantic change detection can be addressed in a unified manner, including quantifying instance-level semantic change and word-level tasks such as measuring the magnitude of semantic change and the broadening or narrowing of meaning.
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