Wiktionary is a project for creating a multilingual, web-based free dictionary of all words in all languages. Like its sister project Wikipedia, since its inception it has been subject to criticism both in terms of its lexicographic approaches and in terms of reliability, content, procedures, and community operation (see Lepore 2006, Fuertes-Olivera 2009, Meyer 2012). Faults have also been pointed in terms of its structure which is confusing for newcomers, with parallel and unaligned information shared among the various language dictionaries, and differences in accuracy and depth among languages. Notwithstanding, data from Wiktionary is routinely employed with successful results in natural language processing and, occasionally, in linguistic research (see Otte 2011, Schlippe 2012, Medero 2009, Li 2012), as it constitutes, by far, the largest free multilingual lexical source.
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.