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        <title>ES-LDA: Entity Summarization using Knowledge-based Topic Modeling</title>
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        <namePart type="given">Seyedamin</namePart>
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    <name type="personal">
        <namePart type="given">Mehdi</namePart>
        <namePart type="family">Allahyari</namePart>
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    <name type="personal">
        <namePart type="given">Krzysztof</namePart>
        <namePart type="family">Kochut</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Hamid</namePart>
        <namePart type="given">Reza</namePart>
        <namePart type="family">Arabnia</namePart>
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            <namePart type="family">Kondrak</namePart>
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    <abstract>With the advent of the Internet, the amount of Semantic Web documents that describe real-world entities and their inter-links as a set of statements have grown considerably. These descriptions are usually lengthy, which makes the utilization of the underlying entities a difficult task. Entity summarization, which aims to create summaries for real-world entities, has gained increasing attention in recent years. In this paper, we propose a probabilistic topic model, ES-LDA, that combines prior knowledge with statistical learning techniques within a single framework to create more reliable and representative summaries for entities. We demonstrate the effectiveness of our approach by conducting extensive experiments and show that our model outperforms the state-of-the-art techniques and enhances the quality of the entity summaries.</abstract>
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%0 Conference Proceedings
%T ES-LDA: Entity Summarization using Knowledge-based Topic Modeling
%A Pouriyeh, Seyedamin
%A Allahyari, Mehdi
%A Kochut, Krzysztof
%A Cheng, Gong
%A Arabnia, Hamid Reza
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F pouriyeh-etal-2017-es
%X With the advent of the Internet, the amount of Semantic Web documents that describe real-world entities and their inter-links as a set of statements have grown considerably. These descriptions are usually lengthy, which makes the utilization of the underlying entities a difficult task. Entity summarization, which aims to create summaries for real-world entities, has gained increasing attention in recent years. In this paper, we propose a probabilistic topic model, ES-LDA, that combines prior knowledge with statistical learning techniques within a single framework to create more reliable and representative summaries for entities. We demonstrate the effectiveness of our approach by conducting extensive experiments and show that our model outperforms the state-of-the-art techniques and enhances the quality of the entity summaries.
%U https://aclanthology.org/I17-1032
%P 316-325

Markdown (Informal)

[ES-LDA: Entity Summarization using Knowledge-based Topic Modeling](https://aclanthology.org/I17-1032) (Pouriyeh et al., IJCNLP 2017)

• ES-LDA: Entity Summarization using Knowledge-based Topic Modeling (Pouriyeh et al., IJCNLP 2017)

ACL

• Seyedamin Pouriyeh, Mehdi Allahyari, Krzysztof Kochut, Gong Cheng, and Hamid Reza Arabnia. 2017. ES-LDA: Entity Summarization using Knowledge-based Topic Modeling. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 316–325, Taipei, Taiwan. Asian Federation of Natural Language Processing.