# Evaluating cross-lingual textual similarity on dictionary alignment This repository contains the scripts to prepare the resources as well as open source implementations of the methods. Word Mover's Distance and Sinkhorn implementations are extended from [Cross-lingual retrieval with Wasserstein distance](https://github.com/balikasg/WassersteinRetrieval). ## Requirements ```bash pip install -r requirements.txt ``` - Python 3 - [nltk](http://www.nltk.org/) ```python import nltk nltk.download('wordnet') ``` - [lapjv](https://pypi.org/project/lapjv/) - [POT](https://pypi.org/project/POT/) - [mosestokenizer](https://pypi.org/project/mosestokenizer/) - (Optional) If using VecMap * NumPy * SciPy
We recommend using a virtual environment

In order to create a [virtual environment](https://docs.python.org/3/library/venv.html#venv-def) that resides in a directory `.env` under home; ```bash cd ~ mkdir -p .env && cd .env python -m venv evaluating source ~/.env/evaluating/bin/activate ``` Inside the virtual environment, the python interpreter and the installed packages are isolated. In order to install all dependencies automatically use the [pip](https://pypi.org/project/pip/) package installer using `requirements.txt`, which resides under the repository directory. ```bash # under Evaluating-Dictionary-Alignment pip install -r requirements.txt ``` Rest of this README assumes that you are on the repository root directory.

## Acquiring The Data ```bash git clone https://github.com/yigitsever/Evaluating-Dictionary-Alignment.git && cd Evaluating-Dictionary-Alignment ./get_data.sh ``` This will create two directories; `dictionaries` and `wordnets`. Linewise aligned definition files are in `wordnets/ready`. ## Acquiring The Embeddings We use [VecMap](https://github.com/artetxem/vecmap) on [fastText](https://fasttext.cc/) embeddings. You can skip this step if you are providing your own polylingual embeddings. Otherwise; * initialize and update the VecMap submodule; ```bash git submodule init && git submodule update ``` * make sure `./get_data` is already run and `dictionaries` directory is present. * run; ```bash ./get_embeddings.sh ``` Bear in mind that this will require around 50 GB free space.