Using support vector machines and state-of-the-art algorithms for phonetic alignment to identify cognates in multi-lingual wordlists (Jäger, List and Sofroniev)
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README.md

svmcc

This repository accompanies the paper Using support vector machines and state-of-the-art algorithms for phonetic alignment to identify cognates in multi-lingual wordlists by Jäger, List and Sofroniev. The repository contains both the data and the source code used in the paper's experiment.

data

datasets

The datasets are located in the data/datasets directory.

dataset language families entries source
abvd Austronesian 12414 Greenhill et al, 2008
afrasian Afro-Asiatic 790 Militarev, 2000
bai Sino-Tibetan 1028 Wang, 2006
central_asian Turkic, Indo-European 15903 Manni et al, 2016
chinese_1964 Sino-Tibetan 3632 Běijīng Dàxué, 1964
chinese_2004 Sino-Tibetan 2789 Hóu, 2004
huon Trans-New Guinea 1176 McElhanon, 1967
ielex Indo-European 11479 Dunn, 2012
japanese Japonic 1963 Hattori, 1973
kadai Tai-Kadai 400 Peiros, 1998
kamasau Torricelli 271 Sanders, 1980
lolo_burmese Sino-Tibetan 570 Peiros, 1998
mayan Mayan 2841 Brown, 2008
miao_yao Hmong-Mien 208 Peiros, 1998
mixe_zoque Mixe-Zoque 961 Cysouw et al, 2006
mon_khmer Austroasiatic 1424 Peiros, 1998
ob_ugrian Uralic 2006 Zhivlov, 2011
tujia Sino-Tibetan 498 Starostin, 2013

Each dataset is stored in a tsv file where each row is a word and the columns are as follows:

column info
language The word's doculect.
iso_code The ISO 639-3 code of the word's doculect; can be empty.
gloss The word's meaning as described in the dataset.
global_id The Concepticon ID of the word's gloss.
local_id The dataset's ID of the word's gloss.
transcription The word's transcription in either IPA or ASJP.
cognate_class The ID of the set of cognates the word belongs to.
tokens The word's phonological segments, space-separated.
notes Field for additional information; can be empty.

The datasets are published under a Creative Commons Attribution-ShareAlike 4.0 International License and can also be found in Zenodo.

vectors

The data/vectors directory contains the samples and targets (in the machine learning sense) derived from the datasets, in csv format. With the exception of central_asian, which is split into two because its size exceeds 100 MB, there is a single vector file per dataset (note that the code will not split this file for you). In these files each row comprises a pair of words from different languages but with the same meaning. The features are described in section 4.3 of the paper.

inferred

The data/inferred directory contains the SVM-inferred cognate classes for each dataset, one .svmCC.csv file per dataset. It also contains the cognacies inferred using the LexStat algorithm, one .lsCC.csv file per dataset.

params

The data/params directory contains the parameters used for inferring the PMI features of the aforementioned feature vectors. For more information, refer to Jäger (2015).

code

The code directory contains the source code used to run the study's experiment. It is Python 3 code and needs NumPy, LingPy, scikit-learn, biopython, and pandas as direct dependencies. You should use requirements.txt to install the dependencies, as the code is only guaranteed to work with the specified versions of those.

setup and usage

# clone this repository
git clone https://github.com/evolaemp/svmcc

# you do not need to create a virtual environment if you know what you are
# doing; remember that the code is written in python3
virtualenv path/to/my/venv
source path/to/my/venv/bin/activate

# install the dependencies
# it is important to use the versions specified in the requirements file
pip install -r requirements.txt

# this ensures the reproducibility of the results
export PYTHONHASHSEED=0

# use manage.py to invoke the commands
python manage.py --help

commands

python manage.py prepare <dataset> reads a dataset, generates its samples and targets, and writes a vector file ready for svm consumption; data/vectors is the default output directory.

python manage.py infer --svmcc reads a directory of vector files, runs svm-based automatic cognate detection, and writes the inferred classes into an output directory; the default input and output directories are data/vectors and data/inferred, respectively.

python manage.py test runs some unit tests.

licence

The source code (but not the data) is published under the MIT Licence (see the LICENCE file).

links