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SynFlow is an open-source toolkit designed for multidimensional diachronic semantic analysis, allowing researchers to examine lexical semantic change (LSC) through a unified framework that integrates various linguistic dimensions. By converting linguistic observations into period-specific distributions and employing a shared analytical workflow, SynFlow enhances the interpretability of semantic shifts across syntactic behavior, morphology, and constructional patterns. A qualitative case study on the German adjective "viral" illustrates how semantic developments can be comprehensively analyzed, revealing insights that traditional vector-space approaches may overlook.
SynFlow reveals the intricate interplay of syntax, morphology, and semantics in lexical change, offering a holistic view that traditional methods miss.
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.
