Novelty detection in text corpora
Pythia is Lab41's exploration of approaches to novel content detection. We are interested in making it easier to tell when a document coming into a corpus has something new to say. We welcome your contributions (see our contributor guidelines) and attention.
Run a quick experiment
You can get started very quickly on a system with Docker using the following commands to pull our publicly available image and train an XGBoost model on the sample data that comes with the repository:
docker pull lab41/pythia docker run -it lab41/pythia experiments/experiments.py with XGB=True BOW_APPEND=True BOW_PRODUCT=True
Tests and building
docker build -t lab41/pythia . # runs tests and builds project image
Our code is written in Python 3. It requires a recent version of Anaconda, as well as a C/C++ compiler system, e.g. GNU gcc/g++ (available in package
build-essential on Ubuntu/Debian systems).
Once these have been installed on your system, envs/make_envs.sh will install the necessary Python dependencies in an Anaconda environment called
The Docker-based distribution comes prepackaged with all necessary dependencies, provided Docker itself is available.
Prebuilt documentation available at http://lab41.github.io/pythia