In recent years, studies exploring the phenomenon of colexification across languages have steadily increased in number. Colexification occurs if a word has multiple meanings, regardless of whether the meanings are related (dish ‘plate; meal’) or unrelated (bank ‘financial institution; part of a river’). The investigation of cross-linguistic colexifications yields many interesting findings that are important for different research fields. Psychologists and cognitive scientists are interested in the overarching principles that establish a connection between meanings and how speakers categorize the environment around them. Historical linguists are concerned with diachronic processes that lead to semantic shifts and what these can tell us about language evolution. Typologists engage in the study of language contact scenarios and how linguistic areas are formed. All these processes are entwined with one another and disentangling them is a challenge. This blog post is the first step into a deeper exploration of the origins of cross-linguistic colexifications and discusses the four processes underlying this phenomenon.
The Small Bang represents a recently begun ERC-funded project dedicated to discovering the origins of the Bangime language and Bangande people. The language and its speakers are of particular interest to West African research as Bangime is one of the only isolates spoken in the region and the ancestors of the Bangande are also unknown. Using the latest computer-assisted technologies, the Bang team is amassing linguistic and genetic data and comparing them with a hitherto unexplored set of languages and peoples in search of a hidden history. Preliminary hypotheses suggest geographic isolation that has led to a bottleneck of at least 9,000 years. The question as to from whom the people and their language originated remains open.
Thanks to the CLDFBench Python package, CLDF datasets compiled with CLDFbench have a rich command-line utility that can easily be expanded by custom commands (Forkel et al. 2018, Forkel and List 2020) . Taking as an example the creation of a Nexus-file for phylogenetic analysis from an existing Lexibank Wordlist (List et al. 2022) , this tutorial will guide you through the necessary steps for writing a script that can be used as a CLDFBench command. You will then be able to apply the same workflow to other scripts you may want to use for a certain repository.
Recently, I was asked by a colleague how one could query only those datasets in the Lexibank repository which come with cognate sets annotated by humans. While I first thought this could be done in a very straightforward way, I figeured out, when trying it myself, that the code still needs some workarounds. As a result, I thought it is best to share the solution I came up with in a blog post in order to make it accessible to colleagues who might be interested in inspecting and using the data provided by the Lexibank repository in more detail.
With the introduction of CLDF as a major format for data exchange, there is an increased need in handy solutions for the conversion of CLDF to the formats required by computer-assisted tools like LingPy and EDICTOR, which allow to preprocess data automatically or to curate data by adding detailed annotations. With the publication PyEDICTOR, there is now a very lightweight software package that is supposed to provide first solutions for a successful integration of CLDF with these existing tools for computer-assisted language comparison.
A colexification network is a network consisting of concepts as nodes with weighted edges drawn between the nodes, indicating how often the concepts colexify across the data in a given sample of languages. Having seen how individual colexifications can be computed with the help of the CL Toolkit package in an earlier blog post, we will now see how this code needs to be extended in order to compute colexification networks.
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).
The Computer Language Comparison in Practice blog has grown steadily over the past few years. Each year we had a number of blog posts written by our colleagues on various topics. With the introduction of a PDF copy for every blog post and the publication of an annual volume, these blog posts can be treated like publications in a non-peer-reviewed journal. We hope that our blog will continue to be a valuable publication venue for our colleagues, not only those working in our research group and department, but also outside collaborators, and scholars who are generally interested in the topic and want to share their ideas. To maintain a coherent structure for all posts, this blog post provides a style guide for all future posts. Continue reading
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.
I recently converted the Gothic dictionary written by Wilhelm Streitberg to a CLDF wordlist. Since I was using Windows, I had some difficulties during the conversion progress, which Unix system users may not have to deal with. I thought it would be useful to share my experience here and point out that users of Windows operating systems should be aware of certain aspects when converting data to CLDF.
Mapping concepts to common concept identifiers across resources has become an important task for the aggregation of lexical data from different sources. With the Concepticon, this task has been facilitated due to a specific mapping algorithm by which a concept list can be automatically mapped to the concept sets in the Concepticon reference catalogue in order to be later manually refined. PySem offers an additional possibility to map concepts to the Concepticon, but in contrast to the algorithm used in the Concepticon workflow, the PySem approach can be accessed from within Python applications.
This is a joint post by Fabrício Ferraz Gerardi, Carolina Coelho Aragon, and Stanislav Reichert.
Whereas the choice of vocabulary needed for analyses in computational historical linguistics is often determined by criteria like stability and resistance to borrowing, one might also be interested in culturally relevant concepts because of their value for reconstructing the proto-culture and even population movements of individual language families. Having striven to find concepts to which these criteria apply within the Tupían language family, we came up with a list of 447 concepts. This post presents the complete list and then briefly illustrates some of the criteria employed in choosing them.