This article describes the first release version of a new lexicostatistical database of Northern Eurasia, which includes Europe as the most well-researched linguistic area. Unlike in other areas of the world, where databases are restricted to covering a small number of concepts as far as possible based on often sparse documentation, good lexical resources providing wide coverage of the lexicon are available even for many smaller languages in our target area. This makes it possible to attain near-completeness for a substantial number of concepts. The resulting database provides a basis for rich benchmarks that can be used to test automated methods which aim to derive new knowledge about language history in underresearched areas.

We evaluate the performance of state-of-theart algorithms for automatic cognate detection by comparing how useful automatically inferred cognates are for the task of phylogenetic inference compared to classical manually annotated cognate sets. Our findings suggest that phylogenies inferred from automated cognate sets come close to phylogenies inferred from expert-annotated ones, although on average, the latter are still superior. We conclude that future work on phylogenetic reconstruction can profit much from automatic cognate detection. Especially where scholars are merely interested in exploring the bigger picture of a language family's phylogeny, algorithms for automatic cognate detection are a useful complement for current research on language phylogenies.

In this article we propose a novel method to estimate the frequency distribution of linguistic variables while controlling for statistical non-independence due to shared ancestry. Unlike previous approaches, our technique uses all available data, from language families large and small as well as from isolates, while controlling for different degrees of relatedness on a continuous scale estimated from the data. Our approach involves three steps: First, distributions of phylogenies are inferred from lexical data. Second, these phylogenies are used as part of a statistical model to estimate transition rates between parameter states. Finally, the long-term equilibrium of the resulting Markov process is computed. As a case study, we investigate a series of potential word-order correlations across the languages of the world.

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In this paper we want to show, that the change of the German verb system from Middle High German to New High German can be simulated and explained by an adoption of the Iterated Learning Model. We claim, that the change of the German verb system is due to the frequency of usage and the process of overgeneralization. This is the first application of an Iterated Learning Model simulating the change of "real" German language, so we can also prove the quality of the Iterated Learning Model as an explanatory model for language change.