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        <title>Probabilistic Prototype Model for Serendipitous Property Mining</title>
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        <namePart type="given">Taesung</namePart>
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        <namePart type="given">Seung-won</namePart>
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    <name type="personal">
        <namePart type="given">Zhongyuan</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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        <name type="personal">
            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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    <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.</abstract>
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        <url>https://aclanthology.org/C16-1064</url>
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%0 Conference Proceedings
%T Probabilistic Prototype Model for Serendipitous Property Mining
%A Lee, Taesung
%A Hwang, Seung-won
%A Wang, Zhongyuan
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F lee-etal-2016-probabilistic
%X 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.
%U https://aclanthology.org/C16-1064
%P 663-673

Markdown (Informal)

[Probabilistic Prototype Model for Serendipitous Property Mining](https://aclanthology.org/C16-1064) (Lee et al., COLING 2016)

• Probabilistic Prototype Model for Serendipitous Property Mining (Lee et al., COLING 2016)

ACL

• Taesung Lee, Seung-won Hwang, and Zhongyuan Wang. 2016. Probabilistic Prototype Model for Serendipitous Property Mining. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 663–673, Osaka, Japan. The COLING 2016 Organizing Committee.