Automatic Generation of Trivia Questions

Authors:
Matthew Merzbacher
Affiliations:
-
Venue:
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Year:
2002

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Abstract

We present a (nearly) domain-independent approach to mining trivia questions from a database. Generated questions are ranked and are more "interesting" if they have a modest number of solutions and may reasonably be solved (but are not too easy). Our functional model and genetic approach have several advantages: they are tractable and scalable, the hypothesis space size is limited, and the user may tune question difficulty. This makes our approach suitable for application to other data mining problems. We include a discussion of implementation on disparate data sets.