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        <title>Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora</title>
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    <name type="personal">
        <namePart type="given">Jooyeon</namePart>
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    <abstract>Much of scientific progress stems from previously published findings, but searching through the vast sea of scientific publications is difficult. We often rely on metrics of scholarly authority to find the prominent authors but these authority indices do not differentiate authority based on research topics. We present Latent Topical-Authority Indexing (LTAI) for jointly modeling the topics, citations, and topical authority in a corpus of academic papers. Compared to previous models, LTAI differs in two main aspects. First, it explicitly models the generative process of the citations, rather than treating the citations as given. Second, it models each author’s influence on citations of a paper based on the topics of the cited papers, as well as the citing papers. We fit LTAI into four academic corpora: CORA, Arxiv Physics, PNAS, and Citeseer. We compare the performance of LTAI against various baselines, starting with the latent Dirichlet allocation, to the more advanced models including author-link topic model and dynamic author citation topic model. The results show that LTAI achieves improved accuracy over other similar models when predicting words, citations and authors of publications.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00055</identifier>
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        <date>2017</date>
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%0 Journal Article
%T Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora
%A Kim, Jooyeon
%A Kim, Dongwoo
%A Oh, Alice
%J Transactions of the Association for Computational Linguistics
%D 2017
%V 5
%I MIT Press
%C Cambridge, MA
%F kim-etal-2017-joint
%X Much of scientific progress stems from previously published findings, but searching through the vast sea of scientific publications is difficult. We often rely on metrics of scholarly authority to find the prominent authors but these authority indices do not differentiate authority based on research topics. We present Latent Topical-Authority Indexing (LTAI) for jointly modeling the topics, citations, and topical authority in a corpus of academic papers. Compared to previous models, LTAI differs in two main aspects. First, it explicitly models the generative process of the citations, rather than treating the citations as given. Second, it models each author’s influence on citations of a paper based on the topics of the cited papers, as well as the citing papers. We fit LTAI into four academic corpora: CORA, Arxiv Physics, PNAS, and Citeseer. We compare the performance of LTAI against various baselines, starting with the latent Dirichlet allocation, to the more advanced models including author-link topic model and dynamic author citation topic model. The results show that LTAI achieves improved accuracy over other similar models when predicting words, citations and authors of publications.
%R 10.1162/tacl_a_00055
%U https://aclanthology.org/Q17-1014
%U https://doi.org/10.1162/tacl_a_00055
%P 191-204

Markdown (Informal)

[Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora](https://aclanthology.org/Q17-1014) (Kim et al., TACL 2017)

• Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora (Kim et al., TACL 2017)

ACL

• Jooyeon Kim, Dongwoo Kim, and Alice Oh. 2017. Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora. Transactions of the Association for Computational Linguistics, 5:191–204.