Title | ||
---|---|---|
ePADD: Computational Analysis Software Facilitating Screening, Browsing, and Access for Historically and Culturally Valuable Email Collections. |
Abstract | ||
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ePADD is free and open-source software that supports the computational analysis of email with potential historical or cultural value. The software incorporates techniques from computer science and computational linguistics, including natural language processing, named entity recognition, and other statistical machine learning-associated processes. These functionalities enable ePADD to promote the appraisal, processing, discovery, and delivery of email held by archival repositories and other cultural memory institutions. In November 2015, Stanford Libraries, with partners University of Illinois Urbana-Champaign, Harvard University, University of California, Irvine, and Metropolitan New York Library Council, received three years of funding from the Institute of Museum and Library Services (IMLS) to advance the formation of a national digital platform by further developing ePADD. Now in year two of the grant, Stanford Libraries and its grant partners have continued to improve the programu0027s scalability, usability, and feature set, while simultaneously taking steps to engage and grow the user community. This article provides background on the need for the ePADD software, identifies how ePADD contributes to the national digital platform, informs on the work completed, highlights the ways in which ePADD is being adopted by the community, and identifies next steps and areas for future project development. |
Year | Venue | Field |
---|---|---|
2017 | D-Lib Magazine | Data science,World Wide Web,Computer science,Computational linguistics,Usability,Software,Cultural memory,Metropolitan area,Valuation (finance),Named-entity recognition,Project management |
DocType | Volume | Issue |
Journal | 23 | 5/6 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Josh Schneider | 1 | 0 | 1.01 |
Peter Chan | 2 | 0 | 1.35 |
Glynn Edwards | 3 | 0 | 1.01 |
Sudheendra Hangal | 4 | 536 | 35.73 |