Scientific metadata quality enhancement for scholarly publications

Guo, Chun; Zhang, Jinsong; Liu, Xiaozhong

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https://hdl.handle.net/2142/42066 Copy

Description

Title

Scientific metadata quality enhancement for scholarly publications

Author(s)

• Guo, Chun
• Zhang, Jinsong
• Liu, Xiaozhong

Issue Date

2013-02

Keyword(s)

• keyword inference
• topic modeling
• language model
• mutual information
• information organization
• knowledge management
• information retrieval

Date of Ingest

2013-02-03T02:07:01Z

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.

Publisher

iSchools

Type of Resource

text

Genre of Resource

Conference Poster

Language

en

Permalink

http://hdl.handle.net/2142/42066

DOI

https://doi.org/10.9776/13382

Copyright and License Information

Copyright © 2013 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.

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