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.
|Title of host publication||COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016|
|Subtitle of host publication||Technical Papers|
|Publisher||Association for Computational Linguistics, ACL Anthology|
|Number of pages||11|
|Publication status||Published - 2016|
|Event||26th International Conference on Computational Linguistics, COLING 2016 - Osaka, Japan|
Duration: 2016 Dec 11 → 2016 Dec 16
|Name||COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers|
|Other||26th International Conference on Computational Linguistics, COLING 2016|
|Period||16/12/11 → 16/12/16|
Bibliographical noteFunding Information:
This work was supported by the Yonsei University New Faculty Research Seed Funding Grant of 2015, the Yonsei University Research Fund (Post Doc. Researcher Supporting Program) of 2015 (project no.: 2015-12-0211), and Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No.B0101-16-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)).
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All Science Journal Classification (ASJC) codes
- Computational Theory and Mathematics
- Language and Linguistics
- Linguistics and Language