https://github.com/uudigitalhumanitieslab/timealign

Parallel corpus annotation and visualization
https://github.com/uudigitalhumanitieslab/timealign

annotation django-application parallel-corpus visualization

Last synced: 9 months ago
JSON representation

Parallel corpus annotation and visualization

• Host: GitHub
• URL: https://github.com/uudigitalhumanitieslab/timealign
• Owner: UUDigitalHumanitieslab
• License: mit
• Created: 2016-07-20T12:06:36.000Z (over 9 years ago)
• Default Branch: develop
• Last Pushed: 2024-01-23T00:58:43.000Z (about 2 years ago)
• Last Synced: 2025-05-07T06:05:37.072Z (9 months ago)
• Topics: annotation, django-application, parallel-corpus, visualization
• Language: Python
• Homepage: http://time-in-translation.hum.uu.nl
• Size: 6.16 MB
• Stars: 7
• Watchers: 3
• Forks: 1
• Open Issues: 11
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# TimeAlign

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10409456.svg)](https://doi.org/10.5281/zenodo.10409456)

TimeAlign is a web application designed for cross-linguistic research using parallel corpora.

It provides:

* An annotation interface where similar forms in aligned phrases can be collected by a team of one or more annotators.

* Descriptive statistics and multiple visualization methods for cross-linguistic variation data.

    

A short demo of the annotation interface is available [here](https://time-in-translation.hum.uu.nl/timealign/instructions/1/)

TimeAlign was created as part of the *Time in Translation* research project. For more information, see the [project website](https://time-in-translation.hum.uu.nl).

## Installation

TimeAlign is created with the [Django web framework](https://www.djangoproject.com/) and requires Python 3.

After installing the dependencies for the MySQL database driver (see below), you can install the required python packages by running `pip install -r requirements.txt`

### MySQL Dependencies

If you want to use MySQL as your database backend (recommended) use the following commands to install a database server and the required packages for the python client.

#### CentOS 7.7

    sudo yum install mariadb-server mariadb-devel python3-devel

    sudo yum groupinstall 'Development Tools'

#### Ubuntu 18.04

    sudo apt-get install python3-dev default-libmysqlclient-dev libssl-dev mysql-server

### Setting up TimeAlign in a virtual environment

    # Clone the repository

    git clone [repository URL]

    cd timealign/

    

    # NOTE! When using Pycharm, .env cannot be recognized as a virtual environment folder. Use 'venv' instead.

    # Create a virtual environment

    sudo apt-get install virtualenv

    virtualenv .env

    source .env/bin/activate

    pip install --upgrade pip wheel

    pip install -r requirements.txt

    # Create a database and change the databases section in timealign/settings.py accordingly

    ## Setup database: https://dev.mysql.com/doc/mysql-getting-started/en/#mysql-getting-started-installing

    ## Create user: https://dev.mysql.com/doc/refman/8.0/en/creating-accounts.html

    # Migrate the database

    ## Create project db setting

    cp ./timealign/settings_secret_default.py ./timealign/settings_secret.py

    ## Update information in the 'settings_secret.py', then execute migrate script

    python manage.py migrate

    # Initialize revisions

    python manage.py createinitialrevisions

    # Run the tests

    python manage.py test

If the test runs OK, you should be ready to roll! Run the webserver using:

    # Start the (local) web server

    python manage.py runserver

During debugging, we additionally use the [Django Debug Toolbar](https://django-debug-toolbar.readthedocs.io/). Install it with:

    pip install django-debug-toolbar

And then uncomment the lines referring to the toolbar in `timealign/settings.py`.

## Documentation

You can find ERD diagrams of the applications in [`doc/models`](doc/models/README.md).

General information on the Time in Translation-project can be found on [our website](https://time-in-translation.hum.uu.nl/).

## Citing

If you happen to have used (parts of) this project for your research, please refer to this paper:

[van der Klis, M., Le Bruyn, B., de Swart, H. (2017)](http://www.aclweb.org/anthology/E17-2080). Mapping the Perfect via Translation Mining. *Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers* 2017, 497-502.