用Transformer模型自动识别圣经希伯来文中的文本呼应，提升研究效率。
Intertextual Parallel Detection in Biblical Hebrew: A Transformer-Based Benchmark
- 采用E5、AlephBERT等预训练模型生成词向量，通过相似度匹配检测文本呼应。
- E5在识别呼应段落上表现最佳，AlephBERT则更擅长区分非呼应段落。
- 为古代文本研究提供自动化工具，适合语言学与神学研究者使用。
识别圣经希伯来文（BH）中的文本呼应关系是圣经学术研究的核心，传统方法依赖人工比对，耗时且易出错。本研究评估了E5、AlephBERT、MPNet和LaBSE等预训练的Transformer模型在《希伯来圣经》中检测文本呼应的潜力。聚焦《撒母耳记/列王纪》与《历代志》之间的已知呼应段落，评估各模型生成词嵌入以区分呼应与非呼应段落的能力。基于余弦相似度和沃尔德斯泰因距离（Wasserstein Distance）度量，发现E5在呼应检测上表现优异，而AlephBERT在区分非呼应段落方面更具优势。结果表明，预训练模型可显著提升古代文本中互文关系检测的效率与准确性，为古代语言研究提供新范式。
原文摘要 · Abstract (English)
Identifying parallel passages in biblical Hebrew (BH) is central to biblical scholarship for understanding intertextual relationships. Traditional methods rely on manual comparison, a labor-intensive process prone to human error. This study evaluates the potential of pre-trained transformer-based language models, including E5, AlephBERT, MPNet, and LaBSE, for detecting textual parallels in the Hebrew Bible. Focusing on known parallels between Samuel/Kings and Chronicles, I assessed each model's capability to generate word embeddings distinguishing parallel from non-parallel passages. Using cosine similarity and Wasserstein Distance measures, I found that E5 and AlephBERT show promise; E5 excels in parallel detection, while AlephBERT demonstrates stronger non-parallel differentiation. These findings indicate that pre-trained models can enhance the efficiency and accuracy of detecting intertextual parallels in ancient texts, suggesting broader applications for ancient language studies.
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