Colleagues often ask us how they could receive more detailed information on specific languages and colexifications in the CLICS database. With the publication of the CL Toolkit package, which allows to merge several CLDF datasets on the fly, carrying out analyses on certain parts of the data underlying the CLICS database is now much easier than before. In order to illustrate this, this tutorial shows how colexifications for a selected number of languages can be computed from two distinct datasets that are included in CLICS (Version 3).
Many words have multiple meanings, especially words for body parts. Some languages call the branch of a tree arm and others refer to the roof with the word head. These are instances of colexifications between body and object concepts and they occur across various languages. However, languages vary in terms of the frequency of body~object colexifications in their lexicon. It is, therefore, important to understand the different drivers that can lead to body~object colexifications. One possible factor to consider is the degree of how visually conspicuous a part of the body is. In an ongoing project, I investigate the role of visual salience as a driving force behind body~object colexifications. This blog post gives a brief overview and introduces the concept list that is the basis for my study.
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).
In the last two weeks we had a renewed interest in colexifications, especially in the third generation of the “Database of Cross-Linguistic Colexifications” (Rzymski, Tresoldi, et al., 2020). The attention was due to two different and independent requests in few days. For those unfamiliar, the concept of “colexification” (François, 2008) refers to instances in which a language uses the same lexeme to express more than one comparable concept (e.g., Russian де́рево, which can mean both “tree” and “wood”). The CLICS project, first developed by List et al. (2014), is an offspring of the transparent approaches to standardization, aggregation, and curation of linguistic data that have been promoted within the CLDF framework (Forkel et al., 2018). It uses standardized lexical databases to identify “colexification networks”.
The CLICS database in its current format makes direct use of the data assembled by the Concepticon project in order to aggregate lexical data from different sources. At the same time, the CLICS database itself can be seen as an interesting conceptlist, providing information on concept polysemy and semantic similarity.
LingPy (List et al. 2017) offers a great deal of functions for string manipulation. Although most of those functions are readily documented (see lingpy.org for details), and the basic ideas have also been described in my dissertation (List 2014), it seems that not many users are aware of these additional possibilities, which the library offers.
In the following, I want to illustrate how we can use LingPy to learn something about consonant clusters occurring in the data underlying the CLICS database (List et al. 2018, clics.clld.org). I have illustrated in an earlier post how one can use the CLICS software API to cook one’s own CLICS application. I will thus assume that you know how to install CLICS (following the instructions on our GitHub page) and the data underlying it.
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.