Joint learning of author and citation contexts for computing drift in scholarly documents

Scholarly documents are sources of information on research topics written by academic experts. Topic drift in such scholarly documents is usually linked with the contextual variation in the title or abstract or entire document over time. However, topic distribution over words in different components...

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Vydané v:	International journal of machine learning and cybernetics Ročník 12; číslo 6; s. 1667 - 1686
Hlavní autori:	Vijayarani, J., Geetha, T. V.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021 Springer Nature B.V
Predmet:	Artificial Intelligence Bivariate analysis Brownian motion Citation analysis Citations Complex Systems Computational Intelligence Context Control Documents Drift Engineering Influence Keywords Mechatronics Original Article Pattern Recognition Robotics Semantics Systems Biology Trends Brownian motion Topic drift Topical word embedding Joint learning Context vector Weighted citation
ISSN:	1868-8071, 1868-808X
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Abstract	Scholarly documents are sources of information on research topics written by academic experts. Topic drift in such scholarly documents is usually linked with the contextual variation in the title or abstract or entire document over time. However, topic distribution over words in different components of the document is non-uniform due to the varying impact of authors and citations, and their contribution to drift must be processed accordingly. This paper builds a model that distinguishes the context of a research document based on the author and citation by incorporating relation between topic, author, citation, word and time in the form of author context vector and citation context vector. To infer posterior probabilities, a parallel author cited_author topic model is presented. Continuous time bivariate Brownian motion model is employed for deducing the evolving bivariate topic parameters, specific to the author and citation. The word, topic pairs from the author and citation context vectors are jointly learned to yield topical word embeddings over time conditioned on author and citation contexts. When evaluated with NIPS and business journals datasets, the proposed model identifies topical variations over time precisely compared to other methods. It is found that broadening of topic happens due to the author context, and topic deviation is mainly caused by citation context.
AbstractList	Scholarly documents are sources of information on research topics written by academic experts. Topic drift in such scholarly documents is usually linked with the contextual variation in the title or abstract or entire document over time. However, topic distribution over words in different components of the document is non-uniform due to the varying impact of authors and citations, and their contribution to drift must be processed accordingly. This paper builds a model that distinguishes the context of a research document based on the author and citation by incorporating relation between topic, author, citation, word and time in the form of author context vector and citation context vector. To infer posterior probabilities, a parallel author cited_author topic model is presented. Continuous time bivariate Brownian motion model is employed for deducing the evolving bivariate topic parameters, specific to the author and citation. The word, topic pairs from the author and citation context vectors are jointly learned to yield topical word embeddings over time conditioned on author and citation contexts. When evaluated with NIPS and business journals datasets, the proposed model identifies topical variations over time precisely compared to other methods. It is found that broadening of topic happens due to the author context, and topic deviation is mainly caused by citation context.
Author	Geetha, T. V. Vijayarani, J.
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Cites_doi	10.1007/s00521-020-04761-6 10.1007/s11192-016-2049-4 10.1007/s11704-016-5442-5 10.1016/j.joi.2016.04.002 10.1177/0165551518806116 10.1007/s10618-019-00659-7 10.1016/j.joi.2019.01.010 10.1016/j.joi.2013.12.001 10.1162/tacl_a_00140 10.5815/ijmecs.2020.01.04 10.1162/tacl_a_00055 10.1145/1658377.1658381 10.3233/IDA-163282 10.3233/JIFS-191933 10.1137/1.9781611972832.59 10.1007/s11192-020-03382-z 10.18653/v1/N18-1098 10.1145/1273496.1273526 10.1145/1143844.1143859 10.1609/aaai.v32i1.11745 10.1007/978-3-642-20161-5_82 10.1145/2872427.2883046 10.1109/BIGCOMP.2016.7425797 10.1609/aaai.v31i1.11062 10.1145/3077136.3080806 10.1145/3178876.3185999 10.1145/1150402.1150450 10.1007/s13042-020-01152-0 10.1609/aaai.v29i1.9522
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SubjectTerms	Artificial Intelligence Bivariate analysis Brownian motion Citation analysis Citations Complex Systems Computational Intelligence Context Control Documents Drift Engineering Influence Keywords Mechatronics Original Article Pattern Recognition Robotics Semantics Systems Biology Trends
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