In a previous post (Kaiping 2018), I described how to convert matrix-shape word lists given in Excel into the long format LingPy and other software can work with. My motivation for this was to provide my colleague Yunus Sulistyono with a good way to compare the lexicon of his Alorese [alor1247] dialects, and to understand the relationship between them. In this post, the data is automatically cognate coded and converted into CLDF. Continue reading “From Fieldwork to Trees 2: Cognate coding”
The blog post introduces ideas discussed in our project about taking a closer look at word formation from a semantic (or semasiological) point of view. Since this so far underinvestigated approach to word formation processes lacks proper terminology, a new term to denote the central research question of concept-based type-frequency is introduced and contrasted with related established terminology.
A colleague of mine has recently returned from his fieldwork, where he collected data on on the dialectal variation of the Alorese language of Alor and Pantar in the East Nusa Tenggara province of Indonesia. He collected data on 13 Alorese varieties, including word list data. One obvious step for comparing the dialects is to mark which forms are obviously cognate and then use a standard tree (or network) construction algorithm to display the shared signal in the data. With standard tools and a bit of Python glue, this is an easy task. A script for 3 steps can be found in my repository on github. In this first part, I will describe how to get an Excel file into a format LingPy can deal with.
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.