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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Bibliographic Details
Published in:International journal of machine learning and cybernetics Vol. 12; no. 6; pp. 1667 - 1686
Main Authors: Vijayarani, J., Geetha, T. V.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-020-01265-6