How to do X in linguistics? A new series of blog posts

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.

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A model of distinctive features for computer-assisted language comparison

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.

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Concept Similarity in STARLING

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.

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New Lexical Data for the Kusunda Language

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.

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New Kusunda data: A list of 250 concepts

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.

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RhyAnT: A web-based tool for interactive rhyme annotation

In times where home office is an obligation rather than an option, I have finally found time to create a first draft version of a web-based tool for interactive rhyme annotation. The tool is written in plain JavaScript, without any additional libraries, and supports the inline rhyme annotation format which we proposed in an earlier study. It allows for an efficient and save annotation of poems for their rhyme structure and will hopefully help us to assemble larger samples of rhyme patterns across genres, languages, times, and cultures.

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Adding concept lists to Concepticon: A guide for beginners

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.

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Linguists love plants, too!

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.

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Biological metaphors and methods in historical linguistics (3): Homology and homoplasy

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).

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Illustrating linguistic data reuse: a modest database for semantic distance

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.

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Biological metaphors and methods in historical linguistics (2): Words and genes

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.

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Feature-Based Alignment Analyses with LingPy and CLTS (2)

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) nw_align() or 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.

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