I cannot remember when I decided to become a linguist. I cannot even remember when I first called myself a linguist (as opposed to a student, a Sinologist, or a scientist). But I can remember when I wrote my first review for a linguistics journal, and I also remember that it came close to a catastrophe, since I maintained a very hostile tone, I didn’t like the paper, thought the authors were badly informed, and didn’t want to allow the paper to be published.
Having illustrated how a quick correspondence pattern analysis can be done with help of readily formatted data and the EDICTOR tool alone, it is now time to show how we can use the LingRex package in order to carry out a full-fledged correspondence pattern analysis. While EDICTOR uses a simple algorithm that is based on sorting the patterns, the Python algorithm for correspondence pattern detection, which is described in detail in List (2019), uses a greedy approach inspired by the Welsh-Powell algorithm for graph coloring (Welsh and Powell 1967), in order to cluster all alignment sites in the data into clusters which are compatible with each other.
By comparing the languages of the world, we gain invaluable insights into human prehistory, predating the appearance of written records by thousands of years. The traditional methods for language comparison are based on manual data inspection. With more and more data available, they reach their practical limits. Computer applications, however, are not capable of replacing experts’ experience and intuition. In a situation where computers cannot replace experts and experts do not have enough time to analyse the massive amounts of data, a new framework, neither completely computer-driven, nor ignorant of the help computers provide, becomes urgent. Such frameworks are well-established in biology and translation, where computational tools cannot provide the accuracy needed to arrive at convincing results, but do assist humans to digest large data sets.
After one month of preparation, during which our team was busy teaching each other, members of our seminar at Friedrich Schiller University Jena, and colleagues in our department, how to code, we are ready to launch the first posts during the next weeks.
I will refrain from promising too much at this stage, but I recommend those interested in learning more about different topics as diverse as coding (in Python and R), data curation and analysis, theory of diversity linguistics, and methodology of historical language comparison to keep an eye on this blog. Our core team of four to five authors will try to publish at least one new blogpost per month, and we will try to constantly increase our range of authors by inviting colleagues from our Department of Linguistic and Cultural Evolution and from other institutions to present their questions, ideas, or approaches to questions related to computer-based and computer-assisted approaches in historical language comparison and beyond.
Our team is currently preparing the first blogposts for this month. I won’t tell you too much about the concrete content yet, but if you are interested in computer-assisted language comparison and empirical approaches to diversity linguistics, I recommend you to keep an eye on our weblog.