Did you know?: mining interesting trivia for entities from wikipedia

Trivia is any fact about an entity which is interest-ing due to its unusualness, uniqueness, unexpect-edness or weirdness. In this paper, we propose a novel approach for mining entity trivia from their Wikipedia pages. Given an entity, our system ex-tracts relevant sentences from its Wikipedia page and produces a list of sentences ranked based on their interestingness as trivia. At the heart of our system lies an interestingness ranker which learns the notion of interestingness, through a rich set of domain-independent linguistic and entity based fea-tures. Our ranking model is trained by leveraging existing user-generated trivia data available on the Web instead of creating new labeled data. We eval-uated our system on movies domain and observed that the system performs significantly better than the defined baselines. A thorough qualitative analysis of the results revealed that our rich set of features indeed help in surfacing interesting trivia in the top ranks. 1