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Title:Scientific metadata quality enhancement for scholarly publications
Author(s):Guo, Chun; Zhang, Jinsong; Liu, Xiaozhong
Subject(s):keyword inference
topic modeling
language model
mutual information
information organization
knowledge management
information retrieval
Abstract:Keyword metadata is very important to the access, retrieval, and management of scientific publications. However, author-assigned keywords are not always readily available in digital repositories. In this study, in order to enhance metadata quality, we explore different automatic methods to infer keywords from scholarly articles, including supervised topic modeling, language model, and mutual information. Evaluation results showed that the linear combination of mutual information and topic modeling with full text outperform other methods on MAP, while language model with abstract performed better than other methods on the measure of precision@10.
Issue Date:2013-02
Citation Info:Guo, C., Zhang, J., & Liu, X. (2013). Scientific metadata quality enhancement for scholarly publications. iConference 2013 Proceedings (pp. 777-780). doi:10.9776/13382
Genre:Conference Poster
Publication Status:published or submitted for publication
Peer Reviewed:is peer reviewed
Rights Information:Copyright © 2013 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.
Date Available in IDEALS:2013-02-03

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