GLEM is a lemmatizer for Ancient Greek.
It has been created in the project Unraveling the Language of Perspective, which is supported by the EU under FP7, ERC Starting Grant 338421-Perspective.
The paper 'A memory-based lemmatizer for Ancient Greek' reports on how it works, what material it uses, and what the accuracy is. It can be found in the repository and at http://dl.acm.org/citation.cfm?id=3078100.
A webservice where you can upload texts that you want to have lemmatized can be found at https://webservices.cls.ru.nl/. Or you can host your own.
Just Python 3 for the simple word list based lemmatizer.
To add machine learning based lemmatization that also takes into account the context, glem uses Frog via its python binding.
Run: pip install .
We recommend using a Python virtual environment of your own. Alternatively for a global installation,
prepend sudo.
Glem comes with a pretrained model, based on lemmas chosen by humans (in the UiO PROIEL project, PI: Dag Haug), for Herodotus. You can use it (with or without Frog) as follows:
glem -f input.txt
The files for this model can be found in glem/pretrained_models/herodotus .
A Dockerfile is provided for deployment of the GLEM webservice in production environments.
From the repository root, build as follows:
$ docker build -t webglem .
Consult the Dockerfile for various build-time parameters that you may want to set for your own production environment.
When running, mount the path where you want the user data stored into the container, a directory webglem-userdata will be created here:
$ docker run -p 8080:80 -v /path/to/data/dir:/data webglem