SynFlow：一个多维历时语义分析工具包

中文摘要 AI 辅助

词汇语义变化（LSC）通常通过向量空间表示建模，但这些方法往往难以清晰说明使用中的哪些方面在发生变化。历时语料库研究则会考察可解释维度，如句法行为、形态学和构式模式，但通常是通过独立的分析工作流进行的。我们提出SynFlow，一个用于语言使用多维历时分析的开源工具包。SynFlow将语言观测结果转换为特定时期的分布，并在基于依存关系的共现、形态特征、构式配置以及外部衍生表示（如框架语义）上应用统一工作流。它支持不同的距离度量，以及值级分解、统计检验和词汇填充词的增量聚类。我们通过对德语形容词viral的定性案例研究演示SynFlow，展示单一语义发展如何在句法、词汇、构式和形态维度上体现。我们还报告了SemEval-2020 Task 1的已发表结果，以将这些表示的性能与现有词汇语义变化检测系统进行对比。

英文摘要

Lexical semantic change (LSC) is commonly modelled through vector-space representations, but these approaches often provide limited insight into which aspects of usage are changing. Diachronic corpus research instead examines interpretable dimensions such as syntactic behaviour, morphology, and constructional patterns, but typically through separate analytical workflows. We present SynFlow, an open-source toolkit for multidimensional diachronic analysis of linguistic usage. SynFlow converts linguistic observations into period-specific distributions and applies a shared workflow across dependency-based co-occurrences, morphological features, constructional configurations, and externally derived representations such as Frame Semantics. It supports different distance measures, together with value-level decomposition, statistical testing, and incremental clustering of lexical fillers. We demonstrate SynFlow through a qualitative case study of the German adjective viral, showing how a single semantic development is reflected across syntactic, lexical, constructional, and morphological dimensions. We further report previously published results on SemEval-2020 Task 1 to situate the performance of these representations relative to existing lexical semantic change detection systems.

发表机构

• KU Leuven（鲁汶大学）
• Instituut voor de Nederlandse Taal（荷兰语言研究所）
• Vrije Universiteit Brussel（布鲁塞尔自由大学）

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