@inproceedings{Moore:2020:POST,
 abstract = {Organizing and assigning sessions within a large confer- ence is a formidable challenge. Some conference organiz- ers, who are typically volunteers, have utilized event plan- ning 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 process- ing to find the topics within a corpus of conference submis- sions and then cluster them together into sessions. As a preliminary evaluation of this technique, we compare ses- sion assignments from previous conferences to ones gener- ated with our proposed techniques.},
 address = {New York, NY, USA},
 author = {Nathan Moore and Kevin Molloy and William Lovo and Sven Mayer and Pawel W. Wozniak and Michael Stewart},
 booktitle = {Companion of the 2020 ACM International Conference on Supporting Group Work},
 date = {2020-01-06},
 doi = {https://doi.org/10.1145/3323994.3369892},
 keywords = {machine learning},
 pages = {135-138},
 publisher = {ACM},
 pubstate = {published},
 series = {GROUP'20},
 title = {POST: A Machine Learning Based Paper Organization and Scheduling Tool},
 tppubtype = {inproceedings},
 url = {http://sven-mayer.com/wp-content/uploads/2020/01/moore2020post.pdf},
 year = {2020}
}