🤖 AI Summary
This study investigates how dataset characteristics constrain the effectiveness of quantitative methods in detecting semantic change within historical linguistics. By comparing two diachronic approaches—quadripartite conceptual modeling applied to the EEBO-TCP corpus and SynFlow analysis on the Royal Society Corpus—the research examines differences in conceptual operationalization, underlying data assumptions, and diachronic interpretability. The findings highlight the limitations of purely lexical frequency-based methods and demonstrate that data structure, including temporal granularity and textual representativeness, fundamentally determines which types of semantic shifts can be reliably identified. This work thus offers methodological guidance for historical semantic research and advocates for the development of quantitative paradigms better aligned with the specific properties of historical linguistic data.
📝 Abstract
This discussion paper reflects on how quantitative approaches to historical linguistics interact with dataset properties. Drawing on two worked examples, we examine English data using quad-based concept modelling of Early Modern English discourse in EEBO-TCP (c. 1470s-1690s; 765M words) alongside SynFlow analysis of scientific writing in Royal Society Corpus 6.0.4 (1750-1799; drawn from a 78.6M-token open corpus). Through parallel comparison, the paper explores how each approach operationalises concepts, the data assumptions they entail, and the diachronic interpretations they support. We argue that comparative methodological reflection clarifies the limits of purely lexical, frequency-based approaches and highlights how dataset structure shapes the kinds of semantic change that quantitative methods can reliably detect.