This post introduces a model of segmental/distinctive features for the symbolic representation of sounds, covering almost 600 segments from CLTS (List et al., 2019) mapped to unique sets of bivalent features. It is being designed as an alternative input to vectors of presence/absence built from BIPA descriptors, analogous to other feature matrices like the one by Phoible (Moran & McCloy, 2019). While still under development, it can already be used both for training models of machine learning and statistics, notably decision trees, and for bootstrapping language- and process-specific models, aided by an “universal” and concise reference. The complete matrix is available on Zenodo. Asupporting Python library, distfeat, is available on PyPI.
In lexicography, there are two commonly used types of semantic data categorization: semantic domains and semantic labels. The difference between the two approaches is simple: semantic domains presume that each lexical meaning belongs to one and only one group (or sub-group of a larger group). Semantic labels do not have such limitations.
STARLING is a software package, originally created by Sergej A. Starostin, which is designed for historical linguists who want to build their own etymological dictionaries. It is not only a database system that allows its users to set up a very straightforward relational database structure, but also a package full of surprises, since it contains many methods that are supposed to automate specific tasks in historical linguistics. These range from phylogenetic tree reconstruction via the preliminary identification of sound correspondences up to the comparison of elicitation glosses for their semantic similarity. While phylogenetic reconstruction and sound correspondences are now quite successfully handled in alternative software packages, I thought it would be interesting to discuss the routine for assessing concept similarity in more detail, since it offers interesting possibilities for those who practice historical language comparison.
Endangered language documentation and endangered language revitalisation have been two hot topics in recent years. For instance, the United Nations Educational, Scientific and Cultural Organization (UNESCO) declared the year 2019 as the International Year of Indigenous Languages. However, although the UNESCO and many other organizations (e.g. The Endangered Languages Documentation Programme or SIL International) urge the public to be aware of the rapidly decreasing number of languages in the world, it does not slow down the annual rate of language loss. For example, the total number of speakers of the Kusunda language, a moribund language spoken in Nepal, decreased to only one person in 2020.
This is a joint post by Uday Raj Aaley (independent researcher, Dang, Nepal) and Timotheus A. Bodt.
Between 29th July 2019 and 12th August 2019, we invited the then remaining two speakers of the Kusunda language to Kathmandu, where we interviewed them. One of these speakers, Gyani Maiya Sen Kusunda, unfortunately passed away early 2020. At the moment of writing this, there is only one Kusunda speaker left, Kamala Sen Kusunda.
The CLICS database in its current format makes direct use of the data assembled by the Concepticon project in order to aggregate lexical data from different sources. At the same time, the CLICS database itself can be seen as an interesting conceptlist, providing information on concept polysemy and semantic similarity.
Scientific data should be openly accessible. This includes databases which are designed for collaborative work. However, in most cases, these databases are only extended by a team of experts. If a database is truly collaborative, the workflows need to be accessible for everybody. The Concepticon database (List et al., 2019) invites contributors to include their own data sets. This requires a transparent description of the contributing process.
While the core of the Concepticon project (https://concepticon.clld.org, List et al. 2019) are the numerous conceptlists which are constantly being added by the growing list of contributors, we have already from the beginning of the project, with the first version (List et al. 2016) tried to collect various kinds of concept metadata for all our concept sets.
Linguists can never solely concentrate on language. This is especially true for field linguists who document a previously unknown language. Unveiling the charm of an undocumented language requires the researcher to explore as much as possible about the people, the society, and of course, the nature around them.
As we have seen in previous instances of this blog post series, there are many parallels but also many differences between the evolutionary branches of biology and of linguistics. In the following, I will present a comparison of the causes due to which two related inheritable entities (e.g. two words or two genes of different languages or species) may differ from each other, or two unrelated ones resemble each other. The linguistic categories presented here can also be found in List (2016) whereas the biological categories are largely based on Koonin (2005).
Besides new algorithms and tools that facilitate established workflows, one change prompted by computer-assisted approaches to language comparison is a distinct relationship between scientists and their data. A critical part of our work, and perhaps the one with the most lasting impact, is to promote an approach in which the data life-cycle is not constrained within the limits of planning and publishing a study. Data are organized and planned for reuse in investigations perhaps not even considered during collection, with the output of one project becoming the input of another.
As was mentioned in the introduction to this series of blogposts, both species and languages are often presented in a tree model. In biology, trees of each individual gene are created in order to account for horizontal transmission and other processes in which the history of a gene differs from the general history of its genome. From the sum of these trees, the species trees are then derived, a method called gene tree reconciliation (Nakhleh 2013). In linguistics on the other hand, phylogenetic trees normally are built on cognate sets of related words, from which the most likely tree of the languages is calculated. A closer equivalent however would be to describe the history of each individual word or word form, including regular sound change, irregular changes to its form, semantic changes, borrowings, and processes of word formation, and to derive the language tree based on the sum of the word histories (Gray, Greenhill, and Ross 2007, 15). Unlike its biological equivalent, this is normally done manually.
Having seen how we can obtain a simple scorer derived from the feature system in CLTS (List et al. 2019) in last month’s post, what is missing now, in order to use the scorer for alignment analyses, is an alignment function which can take the scorer as an argument. If one does not have higher ambitions with respect to the alignment function itself, this step can be achieved in a very straightforward way with help of LingPy’s (List et al. 2018)
sw_align() method. As can be seen from the documentation, this method takes as input two sequences (i.e., lists of sounds), along with a scoring function. Obviously, all we need to do now is to create our specific scorer based on the CLTS features, and then pass this scoring function along with our sequences to the function.
In the past, people have repeatedly asked me how they could use their own scoring functions in combination with LingPy’s alignment algorithms. Their major concern was that the sound-class-based scoring systems we use in LingPy might fail to reflect true phonetic similarity of sounds, specifically also because they are not informed by classical ideas about distinctive features in phonology. As described in detail in List (2014), LingPy converts sounds in phonetic transcription to an internal alphabet of less than 30 letters, to which the alignment algorithms are then applied in a second stage.