Unsupervised machine learning for conference scheduling : a natural language processing approach based on latent dirichlet allocation

Abstract

Academic conference scheduling is the act of organizing large-scale conferences based upon the submission of academic papers in which the author will provide a talk. Traditionally each speaker is placed into a session where other similarly themed talks will take place. To create an appropriate conference schedule, these talks should be organized by thematic similarity. This requires conference organizers to read through abstracts or extended abstracts of submissions to understand how to place these papers together in a cohesive manner. In very large conferences where the number of submissions may be over several hundred, this proves to be a demanding task as it requires considerable time and effort on behalf of organizers. To help automate this process, this thesis will utilize a form of topic modeling called latent Dirichlet allocation which lies in the realm of natural language processing. Latent Dirichlet allocation is an unsupervised machine learning algorithm that analyzes text for underlying thematic content of documents and can assign these documents to topics. This can prove to be a tremendously beneficial tool for conference organizers as it can reduce the required effort to plan conferences with minimal human intervention if executed correctly. To examine how this method of topic modeling can be applied to conference scheduling, three different conferences will be examined using textual data found within the submitted papers to these conferences. The goal of creating these topic models is to understand how latent Dirichlet allocation can be used to reduce required effort and see how data set attributes and model parameters will affect the creation of topics and allocation of documents into these topics. Using this method resulted in clear cohesion between documents placed into topics for data sets with higher average word counts. Improvements to these models exist that can further increase the ability to separate documents more cohesively. Latent Dirichlet allocation proves to be a useful tool in conference scheduling as it can help schedulers create a baseline conference with considerable speed and minimal effort. With this baseline conference created, schedulers are then able to expand upon the results to help create the full conference schedule.

Keywords: natural language processing, conference scheduling, machine learning, latent Dirichlet allocation