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.