Probabilistic prototype model for serendipitous property mining

Taesung Lee, Seung Won Hwang, Zhongyuan Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationCOLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016
Subtitle of host publicationTechnical Papers
PublisherAssociation for Computational Linguistics, ACL Anthology
Pages663-673
Number of pages11
ISBN (Print)9784879747020
Publication statusPublished - 2016
Event26th International Conference on Computational Linguistics, COLING 2016 - Osaka, Japan
Duration: 2016 Dec 112016 Dec 16

Publication series

NameCOLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers

Other

Other26th International Conference on Computational Linguistics, COLING 2016
CountryJapan
CityOsaka
Period16/12/1116/12/16

Bibliographical note

Funding 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)).

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Language and Linguistics
  • Linguistics and Language

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