Historical linguistics traditionally relies on the comparative method, meticulously comparing sounds and words across languages to uncover historical relationships and reconstruct ancestral tongues. While powerful, this approach faces challenges with vast datasets and quantifying uncertainty. Enter computational historical linguistics, a field harnessing the power of data science and algorithms to explore language history on an unprecedented scale.
At its core, this approach treats languages like biological species, adapting phylogenetic methods originally developed for evolutionary biology. Researchers compile large datasets of linguistic features – often basic vocabulary lists (like the Swadesh list), but increasingly also grammatical structures and sound patterns – from numerous languages. These features are coded into formats that computers can process.
Algorithms then analyze this data to build family trees, or phylogenies, showing how languages are related and estimating when they diverged. Bayesian inference methods are particularly popular, allowing researchers not only to construct the most likely tree but also to quantify the uncertainty associated with different branching points and divergence dates. This provides a more nuanced picture than traditional methods alone might offer.
These computational models can simulate language change processes, such as sound shifts and word borrowing or replacement, over time. By comparing different models, linguists can test hypotheses about the rates and mechanisms of language evolution. For instance, models can estimate how quickly core vocabulary changes or how susceptible languages are to borrowing words from neighbours.
Crucially, these language trees often mirror patterns of human migration and population splits. By comparing the structure and dates derived from language phylogenies with evidence from archaeology, genetics, and historical records, researchers can reconstruct ancient population movements and interactions. For example, the branching pattern of the Indo-European language family tree has been used to test theories about its origins and spread, whether from the Anatolian region with the advent of farming or from the Pontic-Caspian steppe.
Recent advancements focus on incorporating more complex data, moving beyond just vocabulary to include syntactic and morphological features. Researchers are also developing more sophisticated models to account for language contact and borrowing (horizontal transfer), which can complicate simple tree-like models of descent. The integration of linguistic data with large-scale genetic datasets is another exciting frontier, offering the potential to create a more unified narrative of human prehistory.
Computational historical linguistics doesn't replace traditional methods but powerfully complements them. It allows linguists to handle massive amounts of data, rigorously test hypotheses, quantify uncertainty, and uncover large-scale patterns in language evolution and its connection to the deep history of human populations. By applying data science, we gain clearer insights into how languages, and the people who spoke them, diversified and spread across the globe.