A colexification network is a network consisting of concepts as nodes with weighted edges drawn between the nodes, indicating how often the concepts colexify across the data in a given sample of languages. Having seen how individual colexifications can be computed with the help of the CL Toolkit package in an earlier blog post, we will now see how this code needs to be extended in order to compute colexification networks.
In a recent blogpost (Tjuka 2021), I introduced a list of color, emotion, and human body part concepts. The list is the basis for a study on colexifications across three semantic domains. The first version of the list included 192 concepts. Of these concepts, 34 were from the emotion domain. We decided to add 28 additional emotion concepts to the list because this will allow us to further improve our analysis and compare our results with previous studies on emotion semantics. This blog post introduces the extended list.
Color, emotion, and human body parts are often considered universal semantic domains. However, cross-linguistic comparison of the connections within each of the domains reveals interesting differences across languages (e.g., Jackson et al. 2019). Researchers in lexical typology systematically collect the names for colors, emotions, and human body parts across various languages and analyze the principles of how naming patterns evolve. Using the same word for multiple concepts or colexification, for example, Vietnamese xanh denoting the colors green and blue, is often discussed in those studies. To study the colexifications across the three semantic domains color, emotion, and human body part, we need a set of comparable concepts. This blog post introduces a list of 192 concepts across all three domains based on the available concept sets in Concepticon (List et al. 2016, 2021).
Besides new algorithms and tools that facilitate established workflows, one change prompted by computer-assisted approaches to language comparison is a distinct relationship between scientists and their data. A critical part of our work, and perhaps the one with the most lasting impact, is to promote an approach in which the data life-cycle is not constrained within the limits of planning and publishing a study. Data are organized and planned for reuse in investigations perhaps not even considered during collection, with the output of one project becoming the input of another.
Robert Forkel just published a very nice cookbook example for our CLICS database (List et al. 2018f, http://clics.clld.org), where you can find out how to manipulate the data further, apart from just installing it and running it to replicate our analyses.
This cookbook tells you how the underlying SQLITE database is structured and how you can, after installing CLICS and the respective packages, access the data to conduct studies of your own.
As a little example of what you can do with the new CLICS API, let me illustrate in this post, how we can use the old CLICS data (underlying the version 1.0 by List et al. 2014, http://clics.lingpy.org), available from here, in the new application, specifically the standalone that we provide.