Data from: Using network approaches to enhance the analysis of cross-linguistic polysemies

Since long it has been noted that cross-linguistically recurring
polysemies can serve as an indicator of conceptual relations, and quite a
few approaches to model and analyze such data have been proposed in the
recent past. Although – given the nature of the data – it seems natural to
model and analyze it with the help of network techniques, there are only a
few approaches which make explicit use of them. In this paper, we show how
the strict application of weighted network models helps to get more out of
cross-linguistic polysemies than would be possible using approaches that
are only based on item-to-item comparison. For our study we use a large
dataset consisting of 1252 semantic items translated into 195 different
languages covering 44 different language families. By analyzing the
community structure of the network reconstructed from the data, we find
that a majority of the concepts (68%) can be separated into 104 large
communities consisting of five and more nodes. These large communities
almost exclusively constitute meaningful groupings of concepts into
conceptual fields. They provide a valid starting point for deeper analyses
of various topics in historical semantics, such as cognate detection,
etymological analysis, and semantic reconstruction.