Discovering Better AAAI Keywords via Clustering with Community-Sourced Constraints


  • Kelly Moran Google Inc.
  • Byron Wallace Brown University
  • Carla Brodley Tufts University



clustering, crowdsourcing, concept drift, text mining, constraints


Selecting good conference keywords is important because they often determine the composition of review committees and hence which papers are reviewed by whom. But presently conference keywords are generated in an ad-hoc manner by a small set of conference organizers. This approach is plainly not ideal. There is no guarantee, for example, that the generated keyword set aligns with what the community is actually working on and submitting to the conference in a given year. This is especially true in fast moving fields such as AI. The problem is exacerbated by the tendency of organizers to draw heavily on preceding years' keyword lists when generating a new set. Rather than a select few ordaining a keyword set that that represents AI at large, it would be preferable to generate these keywords more directly from the data, with input from research community members. To this end, we solicited feedback from seven AAAI PC members regarding a previously existing keyword set and used these 'community-sourced constraints' to inform a clustering over the abstracts of all submissions to AAAI 2013. We show that the keywords discovered via this data-driven, human-in-the-loop method are at least as preferred (by AAAI PC members) as 2013's manually generated set, and that they include categories previously overlooked by organizers. Many of the discovered terms were used for this year's conference.




How to Cite

Moran, K., Wallace, B., & Brodley, C. (2014). Discovering Better AAAI Keywords via Clustering with Community-Sourced Constraints. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).



Main Track: Machine Learning Applications