The project addressed three central scientific challenges in statistical historical linguistics and linguistic typology:
(1) the development of statistical models capable of explaining diachronic and synchronic linguistic diversity at both the lexical and grammatical levels;
(2) the assessment of the reliability and limits of inference with such models; and
(3) the identification of family-specific historical events, such as sound changes, language contact, or borrowing processes, in a principled computational framework.
(1) Model development.
Over the course of the project, several advanced modeling frameworks were developed and implemented. These include Bayesian phylogenetic models that integrate information about shared ancestry, spatially informed models using Gaussian Processes and autoregressive formulations to capture contact effects, and hierarchical models that enable information to be shared across families and macroareas. In addition, the project introduced computational pipelines for large-scale cognate detection, probabilistic models for sound change and lexical evolution, and hierarchical OU and CTMC models for typological features. These methods were evaluated on a broad set of datasets, including Lexibank, Grambank, ASJP, and multiple family-specific lexical resources.
(2) Model assessment and validation.
Inference quality was systematically investigated using sensitivity analyses, prior predictive and posterior predictive checks, missing-data imputation experiments, and cross-validation. The results demonstrate that the models perform strongly in prediction—particularly in reconstructing missing lexical or typological data—but are more limited when it comes to inferring latent historical variables such as ancestral states or unobserved contact scenarios. These findings nuance earlier optimistic assumptions about typological inference and have led to a more rigorous understanding of the conditions under which reliable inference is possible.
Results, exploitation, and dissemination.
The project produced a substantial set of openly available research outputs, including peer-reviewed articles, family-specific datasets, cognate detection resources, and software tools for phylogenetic inference. All datasets and code repositories have been released as open access. They are already being used by the wider community, and they form a foundation for follow-up projects. Results were disseminated through journal publications, conference presentations, workshops, invited talks, and contributions to international research infrastructures such as Lexibank and CLDF.
