POST: A Machine Learning Based Paper Organization and Scheduling Tool

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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.date.accessioned2023-03-17T22:48:55Z
dc.date.available2023-03-17T22:48:55Z
dc.date.issued2020
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.identifier.doi10.1145/3323994.3369892
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4577
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofCompanion Proceedings of the 2020 ACM International Conference on Supporting Group Work
dc.subjecthci
dc.subjectmachine learning
dc.subjectlda
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.citation.startPage135–138
gi.conference.locationSanibel Island, Florida, USA

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