Supplementary Material for the paper "Challenges of Annotation and Analysis in Computer-Assisted Language Comparison: A Case Study on Burmish Languages|
Python TeX
Switch branches/tags
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
cldf
.gitignore
LICENSE
README.md
README.pdf
burmish-compare.tsv
c_bipartites.py
c_cldf.py
c_compare.py
concepts.tsv
d_bed.tsv
d_stedt.tsv
languages.tsv
o_bipartite.gml
o_network.zip
t_bed-stedt-2.tsv
t_bed-stedt.tsv

README.md

Supplementary Data Accompanying the Paper "Challenges of Annotation and Analysis in Computer-Assisted Language Comparison: A Case Study on Burmish Languages"

This is the supplementary material for the paper. It contains the webapplication that allows you to view the bipartite network, as well as the data and the Python code to create the newtork.

Web-Application

Just unpack the zip folder and open the file index.html in a web-browser.

Python Code

Just run the scripts (using Python3, make sure you have LingPy version 2.6 installed):

$ python c_bipartites.py

Will re-create the file o_bipartite.gml

$ python c_compare.py

Will print the comparison with STEDT which we mention in the paper to the screen and create additional files which are needed for the comparison (all prefixed with a t for "temporary").

Data

Data is given in the following files:

  • concepts.tsv: the concepts linked to Concepticon
  • languages.tsv: the languages linked to Glottolog
  • d_bed.tsv: the BED data which was used for the study
  • d_stedt.tsv: the original STEDT data for the Burmish languages which we extracted for this purpose

Data in CLDF (Forkel and List 2017)

Following the specifications of the CLDF initiative, we provide the data in CLDF format as well. You find the data in the cldf folder. The script we used to convert the data in CLDF-format is c_cldf.py, and you can run it by writing:

$ python3 convert.py

References