Following up on my announcement to present in more detail how the algorithms for automatic correspondence pattern detection can be applied, this post introduces the preliminary preparations needed to run a first experiment with aligned data. In order to avoid that we have to align a dataset completely from scratch, we make use of already aligned data from the Tableaux Phonétiques des Patois Suisses Romands by Gauchat et al. (1925), which were originally aligned for the study in List (2014) and later published as part of the Benchmark Database of Phonetic Alignments(List and Prokić 2014). In this post, I will introduce how we can harvest the alignments from this dataset with help of LingPy, and later analyze them with help of the sound correspondence pattern algorithms.
Imagine you have two different datasets, both containing approximately the same concepts, but slightly different numbers of columns and — more importantly — potentially identical identifiers in the first column. A bad idea for merging these datasets would be to paste them in Excel or some other kind of spreadsheet software, and then trying to manually adjust all problems that might occur during this process.
A better idea is to just use LingPy and our CLDF curation framework Continue reading
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.
LexStat class for cognate detection confuses those who want to apply it, since the name of the Python class is the same as the name of one of the methods the class provides, but the class can be used for other types of cognate detection as well. I recommend all users of LingPy that they give a read to our most recent tutorial on LingPy’s cognate detection method (List et al. 2018), since the three most important methods are discussed there in detail, namely the edit distance method for cognate detection, which makes use of the simple, normalized edit distance, the SCA method, based on the Sound-Class-Based Alignment algorithm (List 2014), and the LexStat method (ibid.). Applying these methods in LingPy is fairly simple and described in detail in our aforementioned tutorial. But LingPy offers an additional method for cognate detection that has the advantage of being extremely fast and thus especially suitable for exploratory data analysis of very large datasets. This method is called
turchin in LingPy, named after the first author of a paper presenting the method (Turchin et al. 2010), but the method itself, which Turchin et al. name “Consonant Class Matching” method, goes originally back to Dolgopolsky (1964)), and has long since been implemented as a part of the STARLING software package (http://starling.rinet.ru/program.php). Continue reading
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.