POST: A Machine Learning Based Paper Organization and Scheduling Tool

dc.contributor.authorMoore, Nathan
dc.contributor.authorMolloy, Kevin
dc.contributor.authorLovo, William
dc.contributor.authorMayer, Sven
dc.contributor.authorWozniak, Pawel W.
dc.contributor.authorStewart, Michael
dc.description.abstractOrganizing and assigning papers into sessions within a large conference is a formidable challenge. Some conference organizers, who are typically volunteers, have utilized event planning software to ensure simple constraints, such as two people can not be scheduled to talk at the same time. In this work, we proposed utilizing natural language processing to find the topics within a corpus of conference submissions and then cluster them together into sessions. As a preliminary evaluation of this technique, we compare session assignments from previous conferences to ones generated with our proposed techniques.en
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofCompanion Proceedings of the 2020 ACM International Conference on Supporting Group Work
dc.subjectmachine learning
dc.subjecttext mining
dc.subjectnatural language processing
dc.subjecthuman computer interaction
dc.titlePOST: A Machine Learning Based Paper Organization and Scheduling Toolen
dc.typeText/Conference Paper
gi.conference.locationSanibel Island, Florida, USA