用神经生物学特征解释词语意义随时间变化的机制。
Improving Interpretability of Lexical Semantic Change with Neurobiological Features
- 将语言模型嵌入映射到神经生物学特征空间，每维代表一个基本语义特征。
- 相比多数先前方法，对词语语义变化程度估计性能更优。
- 可系统解析语义演变类型，适合关注语义演化规律的研究者。
词汇语义变迁（LSC）指词语含义随时间发生改变的现象。现有研究多聚焦于提升语义变迁程度的估算性能，但难以解释具体如何变化。为增强可解释性，本文提出一种方法，将预训练语言模型生成的上下文嵌入映射至神经生物学特征空间。该空间中每个维度对应词语的一种基础语义特征，其值表示该特征的强度，使人类能系统理解语义变迁过程。在语义变迁程度估计任务中，本方法性能优于多数已有方法。此外，凭借高可解释性，我们进行了多项分析，发现此前被忽略的新型语义变迁模式，并能有效筛选具有特定变迁类型的词语。
原文摘要 · Abstract (English)
Lexical Semantic Change (LSC) is the phenomenon in which the meaning of a word change over time. Most studies on LSC focus on improving the performance of estimating the degree of LSC, however, it is often difficult to interpret how the meaning of a word change. Enhancing the interpretability of LSC is a significant challenge as it could lead to novel insights in this field. To tackle this challenge, we propose a method to map the semantic space of contextualized embeddings of words obtained by a pre-trained language model to a neurobiological feature space. In the neurobiological feature space, each dimension corresponds to a primitive feature of words, and its value represents the intensity of that feature. This enables humans to interpret LSC systematically. When employed for the estimation of the degree of LSC, our method demonstrates superior performance in comparison to the majority of the previous methods. In addition, given the high interpretability of the proposed method, several analyses on LSC are carried out. The results demonstrate that our method not only discovers interesting types of LSC that have been overlooked in previous studies but also effectively searches for words with specific types of LSC.
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