Category Archives: Code

Representing Structural Data in CLDF

The Cross-Linguistic Data Formats initiative (CLDF, https://cldf.clld.org, Forkel et al. 2018) has helped a lot in preparing the CLICS² database of cross-linguistic colexifications (https://clics.lingpy.org, List et al. 2018), since linking our data to Concepticon (https://concepticon.clld.org, List et al. 2016) and Glottolog (https://glottolog.org, Hammarström et al. 2018) has provided incredible help in merging the different datasets into a big comparative dataset.

CLDF, however, is not restricted to lexical data, but can also be successfully used to store structural data, although — due to the nature of structural data — it is much more difficult to compare different datasets.

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Cooking with CLICS

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.

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Exporting Sublists from a Wordlist with LingPy and Concepticon

When dealing with linguistic datasets, we may often want to export only a small part of our data, for example, only vocabulary in a certain range, such as the Swadesh list of 200 items or the list of 35 items by Yakhontov (originally published in Starostin 1991. Thanks to the pyconcepticon API and LingPy’s built-in export functions for wordlists, this task can be done just in a few lines of code, as we will see below. If you prefer to see the raw code instead of the step-by-step explanation below, you can find a GitHub Gist here.

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