SynFlow让语言变化分析跨越多个维度，统一处理语法、形态等多方面演变。
SynFlow: A Multidimensional Diachronic Semantic Analysis Toolkit
- 将语法、形态、构式等多维度语言现象统一为周期性分布进行分析。
- 在德语形容词'viral'案例中揭示其语义演变在不同维度上的共现模式。
- 支持多种距离度量与统计检验，适合语言演化研究者使用。
词汇语义变迁通常通过向量空间表示建模，但此类方法难以揭示使用层面的具体变化。历时语料库研究虽关注可解释的维度如句法行为、形态和构式模式，但常采用独立分析流程。本文提出SynFlow——一个开源的多维度历时语言使用分析工具包。SynFlow将语言观察转化为时期特定的分布，统一应用于基于依存关系的共现、形态特征、构式配置以及外部获取的表征（如框架语义）。支持多种距离度量、值级分解、统计检验与增量聚类。通过德语形容词'viral'的定性案例研究，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。同时报告了在SemEval-2020 Task 1中的已有结果，评估该表征相对于现有语义变迁检测系统的性能。
原文摘要 · Abstract (English)
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.
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