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        <title>A Trio Neural Model for Dynamic Entity Relatedness Ranking</title>
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    <name type="personal">
        <namePart type="given">Tu</namePart>
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        <namePart type="given">Tuan</namePart>
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    <name type="personal">
        <namePart type="given">Wolfgang</namePart>
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            <title>Proceedings of the 22nd Conference on Computational Natural Language Learning</title>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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        <name type="personal">
            <namePart type="given">Ivan</namePart>
            <namePart type="family">Titov</namePart>
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    <abstract>Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications. Prior work often studies entity relatedness in a static setting and unsupervised manner. However, entities in real-world are often involved in many different relationships, consequently entity relations are very dynamic over time. In this work, we propose a neural network-based approach that leverages public attention as supervision. Our model is capable of learning rich and different entity representations in a joint framework. Through extensive experiments on large-scale datasets, we demonstrate that our method achieves better results than competitive baselines.</abstract>
    <identifier type="citekey">nguyen-etal-2018-trio</identifier>
    <identifier type="doi">10.18653/v1/K18-1004</identifier>
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%0 Conference Proceedings
%T A Trio Neural Model for Dynamic Entity Relatedness Ranking
%A Nguyen, Tu
%A Tran, Tuan
%A Nejdl, Wolfgang
%Y Korhonen, Anna
%Y Titov, Ivan
%S Proceedings of the 22nd Conference on Computational Natural Language Learning
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F nguyen-etal-2018-trio
%X Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications. Prior work often studies entity relatedness in a static setting and unsupervised manner. However, entities in real-world are often involved in many different relationships, consequently entity relations are very dynamic over time. In this work, we propose a neural network-based approach that leverages public attention as supervision. Our model is capable of learning rich and different entity representations in a joint framework. Through extensive experiments on large-scale datasets, we demonstrate that our method achieves better results than competitive baselines.
%R 10.18653/v1/K18-1004
%U https://aclanthology.org/K18-1004/
%U https://doi.org/10.18653/v1/K18-1004
%P 31-41

Markdown (Informal)

[A Trio Neural Model for Dynamic Entity Relatedness Ranking](https://aclanthology.org/K18-1004/) (Nguyen et al., CoNLL 2018)

• A Trio Neural Model for Dynamic Entity Relatedness Ranking (Nguyen et al., CoNLL 2018)

ACL

• Tu Nguyen, Tuan Tran, and Wolfgang Nejdl. 2018. A Trio Neural Model for Dynamic Entity Relatedness Ranking. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 31–41, Brussels, Belgium. Association for Computational Linguistics.