Abstract
Conference scheduling and organisation is a particularly
laborious task and can be extremely time consuming.
While many online conference platforms allow manual topic
selection, these can be expensive and typically still require that
individual papers be scanned and labelled appropriately before
being assigned to reviewers and relevant conference tracks or
sessions. This paper shows how the bulk of this process can
be automated using topic models. Latent Dirichlet allocation is
applied to learn conference topics directly from documents, and
a clustering algorithm introduced to separate these into suitably
sized conference sessions, determining an appropriate session
topic in the process. Conference tracks can then be scheduled
by maximising the distance between these session topics, thereby
avoiding potential topic conflicts in parallel tracks.