JGAAP is a tool to allow nonexperts to use cutting edge machine learning techniques on text attribution problems.
JGAAP is developed by the Evaluating Variation in Language (EVL) Lab at Duquesne University.
- Understanding Authorship Attribution
- Running your First Experiment
- Running Large Experiments
- Extending JGAAP
Head over to our releases page to get the latest version of JGAAP.
If you need help with JGAAP, please review our forum of past help requests here.
You should always feel free to reach out to us at any time with questions or suggestions at jgaap-support@googlegroups.com.
JGAAP has been released under the AGPLv3.0 and a copy should be included with the source. If it has not been included, a copy can be found at http://www.gnu.org/licenses/agpl.html