用Transformer模型量化古希腊语与拉丁语词汇的语义关联
PhiloBERTA: A Transformer-Based Cross-Lingual Analysis of Greek and Latin Lexicons
- 基于上下文嵌入与角度相似性度量跨语言词义对齐
- 词源相关的词对相似度显著更高，哲学概念尤其稳定
- 为古典语文学提供可量化的概念传播分析方法
我们提出PhiloBERTA，一种基于Transformer的跨语言模型，用于衡量古希腊语与拉丁语词库间的语义关系。通过对经典文本中选定词对的分析，利用上下文嵌入和角度相似性度量识别精确的语义对齐。结果表明，词源相关词对的相似度显著更高，尤其在诸如epistēmē（scientia）和dikaiosynē（iustitia）等抽象哲学概念上表现突出。统计分析显示这些关系具有显著一致性（p = 0.012），词源相关词对的语义保真度明显优于对照组。研究建立了量化分析哲学概念在希腊与拉丁传统间传播的新框架，为古典语文学研究提供了新方法。
原文摘要 · Abstract (English)
We present PhiloBERTA, a cross-lingual transformer model that measures semantic relationships between ancient Greek and Latin lexicons. Through analysis of selected term pairs from classical texts, we use contextual embeddings and angular similarity metrics to identify precise semantic alignments. Our results show that etymologically related pairs demonstrate significantly higher similarity scores, particularly for abstract philosophical concepts such as epistēmē (scientia) and dikaiosynē (iustitia). Statistical analysis reveals consistent patterns in these relationships (p = 0.012), with etymologically related pairs showing remarkably stable semantic preservation compared to control pairs. These findings establish a quantitative framework for examining how philosophical concepts moved between Greek and Latin traditions, offering new methods for classical philological research.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。