用图扩散算法量化语言间词汇相似性，分析多语地区语言关系。
A Graph Diffusion Algorithm for Lexical Similarity Evaluation
- 构建基于音译距离的加权有向图，通过扩散方程计算语言隶属概率
- 输出[0,1]区间值，反映语言对参考语族的词汇相似分布
- 适用于研究多语交融区域的语言关系，如欧洲语言案例
本文提出一种评估给定语言与多个参考语言聚类之间词汇相似性的算法。输入包含一组概念及其在各语言中的翻译，每个参考语言归属于c个语言聚类之一。对每个概念，算法计算各翻译对之间的距离，基于这些距离构建加权有向图，顶点代表语言。随后求解带Dirichlet边界条件的图扩散方程，未知量为从顶点集到ℝᶜ的映射。结果坐标取值于[0,1]区间，可解释为归属各聚类的概率或相对于参考聚类的词汇相似度分布。翻译间距离通过音译转写及改进的Damerau-Levenshtein距离计算。该算法可用于分析多语地区语言间的相互影响。我们以欧洲多种语言为例展示了其应用效果。
原文摘要 · Abstract (English)
In this paper, we present an algorithm for evaluating lexical similarity between a given language and several reference language clusters. As an input, we have a list of concepts and the corresponding translations in all considered languages. Moreover, each reference language is assigned to one of $c$ language clusters. For each of the concepts, the algorithm computes the distance between each pair of translations. Based on these distances, it constructs a weighted directed graph, where every vertex represents a language. After, it solves a graph diffusion equation with a Dirichlet boundary condition, where the unknown is a map from the vertex set to $\mathbb{R}^c$. The resulting coordinates are values from the interval $[0,1]$ and they can be interpreted as probabilities of belonging to each of the clusters or as a lexical similarity distribution with respect to the reference clusters. The distances between translations are calculated using phonetic transcriptions and a modification of the Damerau-Levenshtein distance. The algorithm can be useful in analyzing relationships between languages spoken in multilingual territories with a lot of mutual influences. We demonstrate this by presenting a case study regarding various European languages.
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