Author Archives: Johann-Mattis List

About Johann-Mattis List

Seit Anfang 2023 leite ich den Lehrstuhl für Multilinguale Computerlinguistik in Passau. In meiner Forschung nehme ich generell einen datenbasierten, empirischen und quantitativen Standpunkt in Bezug auf Sprachwandel und Sprachgeschichte ein, mit einem speziellen Fokus auf südostasiatischen Sprachen. Im Gegensatz zu rein computerbasierten Ansätzen versuche ich jedoch, meine Forschung nah an der traditionellen historischen Linguistik und der linguistischen Theorie auszurichten, weshalb ich einen computer-gestützten Ansatz im Gegensatz zu einem rein computer-basierten Ansatz verfolge.

Past and Future of Computer-Assisted Language Comparison in Practice

Our blog “Computer-Assisted Language Comparison in Practice” goes into its seventh year. We reflect on the role the blog played in the past and present and new goals and concrete ideas for the future. The most drastic innovation we initiated is to turn the blog into an open journal, which means that all future and successively also past contributions will be archived in PDF format with digital object identifiers.

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Parsing IPA Transcriptions with CLTS

The Cross-Linguistic Transcription Systems (CLTS, https://clts.clld.org) project serves as a reference catalogue for speech sounds. At the core of the project is a generative method that parses existing IPA transcriptions (or transcriptions in other supported transcription systems) and checks if they conform to the principles and components laid out in the reference catalogue. As a result, Cross-Linguistic Transcription Systems is much more than a simple list of possible speech sounds transcribed in the International Phonetic Alphabet, but a system that allows to generate possible speech sounds and to check if sounds provided in various transcription systems contain problems. This study gives a short overview on the basic ideas that lead to the creation of the database and the parsing method and provides some examples showing how it can be employed in practice.

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Sequence Manipulation with Orthography Profiles in JavaScript

Orthography profiles allow for the explicit simultaneous segmentation and conversion of sequences from one orthography to another. They play a crucial role in the standardization workflows developed as part of the Cross-Linguistic Data Formats initiative, where they are used to convert original orthographies used for language documentation to a strict version of the International Phonetic Alphabet. Given that the basic algorithm by which orthography profiles can be used to segment and convert sequences across orthographies is very straightforward, it can be easily implemented in JavaScript.
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Creating a CLDF Wordlist from Heath et al.’s Dogon Comparative Wordlist

The Dogon and Bangime Linguistics project (https://dogonlanguages.org) offers a large comparative spreadsheet in which translational equivalents for a huge number of concepts are translated into various Dogon languages.  Due to its enormous size, no attempts have been made so far to integrate the spreadsheet with the lexical resources that were compiled as part of the CLDF initiative in order to populate the Lexibank repository. Here, we report a first attempt to circumvent the problems resulting from the size of the spreadsheet by convert not all but parts of the spreadsheet to CLDF Wordlist standards, which allows us to integrate parts of the data with other resources in Lexibank.

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Creating a Standardized Comparative Wordlist of Newari Varieties

Newari is one of the few ancient Sino-Tibetan languages attested in written texts. Since previous studies on the phylogeny of Sino-Tibetan did not take Newari data into account, we felt it is important to close this gap by providing an up-to-date comparative wordlist of Newari varieties. This wordlist has now been finalized in a first version that has additionally been standardized following the recommendations of the Cross-Linguistic Data Formats initiative.
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Querying Datasets with Cognates in the Lexibank 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.

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PyEDICTOR: A Small Python Package that Integrates LingPy, EDICTOR, and CLDF

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.

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How to Visualize Colexification Networks with JavaScript and D3 (How to do X in Linguistics 12)

Having seen how colexifications can be inferred and how colexification networks can be computed in previous posts, this post concludes our mini series in showing how computed colexification networks can be visualized interactively, using a JavaScript application based on the popular visualization library D3.

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How to Compute Colexification Networks with CL Toolkit (How to do X in Linguistics 11)

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.

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How to Compute Colexifications with CL Toolkit (How to do X in Linguistics 10)

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).

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An Animated Demo of the Wagner-Fischer Algorithm for Sequence Alignment

A long time ago I prepared an animated demo of the Wagner-Fischer algorithm for pairwise sequence alignment. Having used the demo to teach phonetic alignment in class, I thought it might be useful to share it officially, as it may also be interesting for colleagues who teach phonetic alignment or rudimentary JavaScript programming.

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How to Map Concepts with the PySem Library

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.

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How to write a term paper in linguistics (How to do X in linguistics 9)

Writing a term paper requires the same scrutiny as writing an article for a journal. As a result, the techniques which apply when writing term papers are very similar to those which apply when writing a journal article, and students should feel encouraged to take the task as seriously as a journal article that scientists send off to peer review. In the following, I will briefly introduce major techniques that help to structure one’s work when writing a term paper and which also help to interact well with one’s supervisor during the writing process.

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Converting the Vietic Dataset by Sidwell and Alwes from 2021 to CLDF

A few days ago, Sidwell and Alwes submitted a very nice dataset on Vietic languages to Zenodo (10.5281/zenodo.5263194). When inspecting the data, I realized that this dataset could be easily converted to our CLDF formats in our new Lexibank standards. Since both authors explicitly invited for discussions of the data and the testing of the results, I thought it would even be better to quickly illustrate the CLDF conversion in a blog post, as this may enable colleagues to do the same with their datasets in the future.

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How to Share Data and Code when Submitting Papers to a Journal: Transparent Data (How to do X in Linguistics 8)

When sharing data and code when submitting papers to a journal, you need to make sure that the reviewers can test and inspect your data as conveniently as possible. In between reviews, you should also be maximally transparent on any changes that have been made to the data or the code underlying your study. When using data that was published elsewhere, this means you should pay specific attention to the versions you have used and make sure they are readily accessible.

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