这篇论文用图网络和概念特征来分析八种语言里的习语，发现它们按语义模式聚类而不是语言，还能用LLM自动标注，对做跨语言NLP和理解习语很有用。
该论文提出一个基于特征图的可解释框架，用于表示八种语言共160个习语的传统意义。每个习语通过二进制概念特征（如包含、隐藏、情感、社会等）进行标注，并使用Jaccard相似度构建加权图。社区检测显示习语按概念模式聚类而非按语言聚类，其结构符合认知语言学预测。该概念网络捕获了分布式嵌入中不存在的独特语义信息，可通过LLM自动标注扩展，并提升下游习语检测性能。跨语言迁移实验表明，仅凭概念相似性即可识别五种语系中的可接受翻译等价物，相比基于嵌入的基线有显著提升。
Conceptual Networks for Cross-Linguistic Idiomatic Expressions:A Feature-Based Graph Approach
We present an interpretable network-based framework for representing idiomatic and figurative meaning across eight typologically diverse languages, totaling 160 conventional expressions, the large majority of which are idiomatic. Each expression is annotated with binary conceptual features (containment, concealment, emotional, social, etc.) derived from cognitive-linguistic theory, and pairwise Jaccard similarities define a weighted graph. Community detection reveals that idioms cluster by conceptual schema rather than by language, producing a structure consistent with cognitive-linguistic predictions. The conceptual network captures unique semantic information not present in distributional embeddings, can be scaled via automatic annotation with LLMs, improves downstream idiom detection, and remains robust when enriched with corpus frequencies. Cross-lingual transfer experiments show that conceptual proximity alone can identify acceptable translation equivalents across five language families, with substantial gains over embedding-based baselines. Ablation studies demonstrate that all three feature dimensions -- schemas, roles, and valence -- contribute non-redundantly to both the network's organizational properties and its performance on idiom detection, and that specific graph-derived signals (community membership, neighbor similarity) are particularly informative. The framework offers an interpretable, cross-linguistically stable representation of idiomatic meaning, combining theoretical grounding with practical utility.