Computer-Assisted Language Comparison (CALC) - Reconciling Computational and Classical Approaches in Historical Linguistics
The ERC-funded research project CALC (Computer-Assisted Language Comparison) establishes a computer-assisted framework for historical linguistics. We pursue an interdisciplinary approach that adapts methods from computer science and bioinformatics for the use in historical linguistics. While purely computational approaches are common today, the project focuses on the communication between classical and computational linguists, developing interfaces that allow historical linguists to produce their data in machine readable formats while at the same time presenting the results of computational analyses in a transparent and human-readable way.
By comparing the languages of the world, we gain invaluable insights into human prehistory, predating the appearance of written records by thousands of years. The traditional methods for language comparison are based on manual data inspection. With more and more data available, they reach their practical limits. Computer applications, however, are not capable of replacing experts' experience and intuition. In a situation where computers cannot replace experts and experts do not have enough time to analyse the massive amounts of data, a new framework, neither completely computer-driven, nor ignorant of the help computers provide, becomes urgent. Such frameworks are well-established in biology and translation, where computational tools cannot provide the accuracy needed to arrive at convincing results, but do assist humans to digest large data sets.
As a litmus test which proves the suitability of the new framework, the project will create an etymological database of Sino-Tibetan languages. The abundance of language contact and the peculiarity of complex processes of language change in which sporadic patterns of morphological change mask regular patterns of sound change make the Sino-Tibetan language family an ideal test case for a new overarching framework that combines the best of two worlds: the experience of experts and the consistency of computational models.