@InProceedings{lee-hwang-wang:2016:COLING,
  author    = {Lee, Taesung  and  Hwang, Seung-won  and  Wang, Zhongyuan},
  title     = {Probabilistic Prototype Model for Serendipitous Property Mining},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {663--673},
  abstract  = {Besides providing the relevant information, amusing users has been an important
	role of the web. Many web sites provide serendipitous (unexpected but relevant)
	information to draw user traffic. In this paper, we study the representative
	scenario of mining an amusing quiz. An existing approach leverages a knowledge
	base to mine an unexpected property then find quiz questions on such property,
	based on prototype theory in cognitive science. However, existing deterministic
	model is vulnerable to noise in the knowledge base. Therefore, we instead
	propose to leverage probabilistic approach to build a prototype that can
	overcome noise. Our extensive empirical study shows that our approach not only
	significantly outperforms baselines by 0.06 in accuracy, and 0.11 in
	serendipity but also shows higher relevance than the traditional
	relevance-pursuing baseline using TF-IDF.},
  url       = {http://aclweb.org/anthology/C16-1064}
}