Join the project team from ePADD, an open source, freely downloadable software package that use machine learning, including natural language processing and named entity recognition, to provide robust access to email archives.
ePADD grew out of MUSE, a personal digital archiving tool developed by Sudheendra Hangal during his PhD work in the Mobisocial Laboratory of Stanford’s Computer Sciences Department. Stanford Libraries saw the potential for similar functionality to be applied towards a tool that supports of archival appraisal, processing, and research, and received two years of grant funding to develop that tool, which was released in June 2015.
In November 2015, Stanford Libraries received a second round of grant funding to further develop the program’s scalability, usability, and feature set.
In this session, Peter Chan (ePADD Project Manager and Digital Archivist, Stanford University) and Josh Schneider (ePADD Community Manager and Assistant University Archivist, Stanford University) will demo the latest beta release and show you how ePADD can help you search, browse, and visualize texts in new and innovative ways to support your research.