PyBor: A Python library for borrowing detection based on lexical language models

pybor is a library to aid in the detection of borrowings in mono- and multi-lingual concept lists. It implements a variety of statistical methods that can be used individually or in combination, intended for supporting expert decisions in a computer-assisted framework.

pybor can be used both as a Python library or as a stand-alone, command-line tool. It is distributed along with example data that can be used to bootstrap other analyses.

Installation and usage

Upon release, it will be possible to install the library as any standard Python package with pip. Currently, the library must be installed from source:

$ pip install .

To use the default wold package, you will also need to install it in edit mode:

$ pip install -e wold/

Some evaluation methods need a copy of the the CLTS data distribution. This can be obtained with the following command (note that this will clone all catalog repositories form GitHub, and as such might take a while over slow connections):

cldfbench catconfig -q

Changelog

Version 0.1:

• First public release

Roadmap

Version 1.0:

• Published version

Community guidelines

While the authors can be contacted directly for support, it is recommended that third parties use GitHub standard features, such as issues and pull requests, to contribute, report problems, or seek support.

Contributing guidelines, including a code of conduct, can be found in the CONTRIBUTING.md file.

Authors and citation

The library is developed by John Miller (ivorydragonspiral@gmail.com), Tiago Tresoldi (tresoldi@shh.mpg.de), and Johann-Mattis List (list@shh.mpg.de).

TT and JML have received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. ERC Grant #715618, Computer-Assisted Language Comparison.

If you use pybor, please cite it as:

Miller, John; Tresoldi, Tiago; List, Johann-Mattis (2020). PyBor, a Python library for borrowing detection based on lexical language models. Version 0.1. Jena.

In BibTeX:

@misc{Miller2020pybor,
  author = {Miller, John and Tresoldi, Tiago and List, Johann-Mattis},
  title = {PyBor, a Python library for borrowing detection based on lexical language models. Version 0.1.},
  howpublished = {\url{https://github.com/lingpy/pybor}},
  address = {Jena},
  year = {2020}
}