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Converting an Artificial Proto-Language into Data for Testing Computational Approaches in Historical Linguistics (Supplementary Material)

The material shared here allows users to test the code described in the original study by List (2024).

List, Johann-Mattis, "Converting an Artificial Proto-Language into Data for Testing Computational Approaches in Historical Linguistics," in Computer-Assisted Language Comparison in Practice, 7.2. URL: https://calc.hypotheses.org/7363, DOI: 10.15475/calcip.2024.2.1.

Installation

Install package dependencies.

LinguList

Querying Datasets with Cognates in the Lexibank Repository

In order to run the code below (code.py), you need to git-clone the lexibank_analysed package (https://github.com/lexibank/lexibank-analysed) and install it using pip. Then, you need to download the script and place it into the lexibank-analysed folder. Before running, you need to run the command cldfbench download cldfbench_lexibank_analysed.py in order to download all individual datasets. After that, you can just run the script typing python code.py. The output looks as follows:

Dataset	Concepts	Languages	Words	Cognates	Singletons
bdpa	519	538	50095	750	0
blustaustronesian	210	20	4358	321	2409
bowernpny	344	190	44876	4494	25054
cals

LinguList

How to Compute Colexification Networks with CL Toolkit (Supplementary Material)

This code example accompanies the Blog Post "How to Compute Colexification Networks with CL Toolkit (How to do X in Linguistics 11)" by List (2022).

You need to install the python-igraph package, networkx, lingpy, and cltoolkit:

$ pip install python-igraph
$ pip install networkx
$ pip install lingpy

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install:
	git clone https://github.com/intercontinental-dictionary-series/ids.git
	git clone https://github.com/lexibank/northeuralex.git
	pip install cltoolkit

colexifications:
	python colexifications.py

LinguList

Code accompanying "Mapping Multi-SimLex to Concepticon"

This code accompanies the blog post "Mapping Multi-SimLex to Concepticon (https://calc.hypotheses.org/2684).

To run the code, you best install cldfbench with pip in a fresh virtual environment.

Then download the Multi-SimLex data at: https://multisimlex.com/data/translation.csv and run the first script:

$ python test.py

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How to work with WALS data in CLDF

This code example accomanies a blog post published as part of the blog "Computer-Assisted Langauge Comparison in Practice" (https://calc.hypotheses.org).

In order to get started, install the WALS dataset in CLDF format with the help of pip (ideally, make sure to use a fresh virtual environment!).

$ pip install -e git+https://github.com/cldf-datasets/wals.git@v2020#egg=cldfbench_wals

Once this has been done, you should be able to run the script wals.py by simply typing:

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Automated Mapping of Metadata to Concepticon

See the blog at https://calc.hypotheses.org for details.

To run the code, just type:

$ python code.py

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

Requirements (can all be installed with PIP)

• lingpy
• pyclts

Run code

LinguList

Feature-Based Alignment Analyses with LingPy and CLTS (1)

Requirements (can all be installed with PIP)

• pyclts
• tabulate

Run code

LinguList

Waterman-Eggert algorithm for Sentence Alignment

This is a short patch for LingPy's Waterman-Eggert implementation and an illustration how the algorithm can be used to carry out the alignment of two sentences provided in phonetic transcription in linguistics. To test this script, make sure to install LingPy and run the following in your terminal:

$ python code.py