PyINSECT stands for PYthon Interoperable SEmantically-driven Contextually-aware analysis Toolkit. It constitutes a graph-based machine learning library, utilizing thw power of n-gram graphs and proximity graphs to represent text, media (and much much more...) to enable efficient and effective classification, clustering, powerful indexing and other analysis and retrieval tasks.
Table of Contents
A represenation of N-grams-graphs in python, inspired from the JInsect toolkit.
- python (tested on 3.7)
python3.7 -m pip install pyinsect
- Basic graph support
- Basic operator support
- Basic similarities support
- Storage abstraction support
- Parallel operators
- Interoperability with mainstream machine learning toolkits
- Code examples
See the open issues for a full list of proposed features (and known issues). Also see the milestones page for all foreseen milestones.
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the Apache 2 License. See LICENSE.txt for more information.
George Giannakopoulos - ggianna@iit.demokritos.gr
Project Link: https://github.com/ggianna/PyINSECT
- Nikiforos Pittaras
- Bill Sioros