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        <title>Identifying Emergent Research Trends by Key Authors and Phrases</title>
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        <namePart type="given">Shenhao</namePart>
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        <namePart type="given">Animesh</namePart>
        <namePart type="family">Prasad</namePart>
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    <name type="personal">
        <namePart type="given">Min-Yen</namePart>
        <namePart type="family">Kan</namePart>
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        <namePart type="given">Kazunari</namePart>
        <namePart type="family">Sugiyama</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Identifying emergent research trends is a key issue for both primary researchers as well as secondary research managers. Such processes can uncover the historical development of an area, and yield insight on developing topics. We propose an embedded trend detection framework for this task which incorporates our bijunctive hypothesis that important phrases are written by important authors within a field and vice versa. By ranking both author and phrase information in a multigraph, our method jointly determines key phrases and authoritative authors. We represent this intermediate output as phrasal embeddings, and feed this to a recurrent neural network (RNN) to compute trend scores that identify research trends. Over two large datasets of scientific articles, we demonstrate that our approach successfully detects past trends from the field, outperforming baselines based solely on text centrality or citation.</abstract>
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        <url>https://aclanthology.org/C18-1022</url>
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        <date>2018-08</date>
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            <start>259</start>
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%0 Conference Proceedings
%T Identifying Emergent Research Trends by Key Authors and Phrases
%A Jiang, Shenhao
%A Prasad, Animesh
%A Kan, Min-Yen
%A Sugiyama, Kazunari
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F jiang-etal-2018-identifying
%X Identifying emergent research trends is a key issue for both primary researchers as well as secondary research managers. Such processes can uncover the historical development of an area, and yield insight on developing topics. We propose an embedded trend detection framework for this task which incorporates our bijunctive hypothesis that important phrases are written by important authors within a field and vice versa. By ranking both author and phrase information in a multigraph, our method jointly determines key phrases and authoritative authors. We represent this intermediate output as phrasal embeddings, and feed this to a recurrent neural network (RNN) to compute trend scores that identify research trends. Over two large datasets of scientific articles, we demonstrate that our approach successfully detects past trends from the field, outperforming baselines based solely on text centrality or citation.
%U https://aclanthology.org/C18-1022
%P 259-269

Markdown (Informal)

[Identifying Emergent Research Trends by Key Authors and Phrases](https://aclanthology.org/C18-1022) (Jiang et al., COLING 2018)

• Identifying Emergent Research Trends by Key Authors and Phrases (Jiang et al., COLING 2018)

ACL

• Shenhao Jiang, Animesh Prasad, Min-Yen Kan, and Kazunari Sugiyama. 2018. Identifying Emergent Research Trends by Key Authors and Phrases. In Proceedings of the 27th International Conference on Computational Linguistics, pages 259–269, Santa Fe, New Mexico, USA. Association for Computational Linguistics.