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.

Based on a recently published large-scale lexicostatistical database, we rank 1,016 concepts by their suitability for inclusion in Swadesh-style lists of basic stable concepts. For this, we define separate measures of basicness and stability. Basicness in the sense of morphological simplicity is measured based on information content, a generalization of word length which corrects for distorting effects of phoneme inventory sizes, phonotactics and non-stem morphemes in dictionary forms. Stability against replacement by semantic shift or borrowing is measured by sampling independent language pairs, and correlating the distances between the forms for the concept with the overall language distances. In order to determine the relative importance of basicness and stability, we optimize our combination of the two partial measures towards similarity with existing lists. A comparison with and among existing rankings suggests that concept rankings are highly data-dependent and therefore less well-grounded than previously assumed. To explore this issue, we evaluate the robustness of our ranking against language pair resampling, allowing us to assess how much volatility can be expected, and showing that only about half of the concepts on a list based on our ranking can safely be assumed to belong on the list independently of the data.

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In speech, the connection between sounds and word meanings is mostly arbitrary. However, among basic concepts of the vocabulary, several words can be shown to exhibit some degree of form–meaning resemblance, a feature labelled vocal iconicity. Vocal iconicity plays a role in first language acquisition and was likely prominent also in pre-historic language. However, an unsolved question is how vocal iconicity survives sound evolution, which is assumed to be inevitable and ‘blind’ to the meaning of words. We analyse the evolution of sound groups on 1016 basic vocabulary concepts in 107 Eurasian languages, building on automated homologue clustering and sound sequence alignment to infer relative stability of sound groups over time. We correlate this result with the occurrence of sound groups in iconic vocabulary, measured on a cross-linguistic dataset of 344 concepts across single-language samples from 245 families. We find that the sound stability of the Eurasian set correlates with iconic occurrence in the global set. Further, we find that sound stability and iconic occurrence of consonants are connected to acquisition order in the first language, indicating that children acquiring language play a role in maintaining vocal iconicity over time. This article is part of the theme issue ‘Reconstructing prehistoric languages'.

TuLiPA - Parsing extensions of TAG with range concatenation grammarsIn this paper we present a parsing framework for extensions of Tree Adjoining Grammar (TAG) called TuLiPA (Tübingen Linguistic Parsing Architecture). In particular, besides TAG, the parser can process Tree-Tuple MCTAG with Shared Nodes (TT-MCTAG), a TAG-extension which has been proposed to deal with scrambling in free word order languages such as German. The central strategy of the parser is such that the incoming TT-MCTAG (or TAG) is transformed into an equivalent Range Concatenation Grammar (RCG) which, in turn, is then used for parsing. The RCG parser is an incremental Earley-style chart parser. In addition to the syntactic anlysis, TuLiPA computes also an underspecified semantic analysis for grammars that are equipped with semantic representations.