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Identifying entities from scientific publications: A comparison of vocabulary- and model-based methods

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  • Yan, Erjia
  • Zhu, Yongjun

Abstract

The objective of this study is to evaluate the performance of five entity extraction methods for the task of identifying entities from scientific publications, including two vocabulary-based methods (a keyword-based and a Wikipedia-based) and three model-based methods (conditional random fields (CRF), CRF with keyword-based dictionary, and CRF with Wikipedia-based dictionary). These methods are applied to an annotated test set of publications in computer science. Precision, recall, accuracy, area under the ROC curve, and area under the precision-recall curve are employed as the evaluative indicators. Results show that the model-based methods outperform the vocabulary-based ones, among which CRF with keyword-based dictionary has the best performance. Between the two vocabulary-based methods, the keyword-based one has a higher recall and the Wikipedia-based one has a higher precision. The findings of this study help inform the understanding of informetric research at a more granular level.

Suggested Citation

  • Yan, Erjia & Zhu, Yongjun, 2015. "Identifying entities from scientific publications: A comparison of vocabulary- and model-based methods," Journal of Informetrics, Elsevier, vol. 9(3), pages 455-465.
    • Handle: RePEc:eee:infome:v:9:y:2015:i:3:p:455-465
    DOI: 10.1016/j.joi.2015.04.003
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    References listed on IDEAS

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    1. Staša Milojević & Cassidy R. Sugimoto & Erjia Yan & Ying Ding, 2011. "The cognitive structure of Library and Information Science: Analysis of article title words," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(10), pages 1933-1953, October.
    2. Don R. Swanson & Neil R. Smalheiser & Vetle I. Torvik, 2006. "Ranking indirect connections in literature‐based discovery: The role of medical subject headings," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(11), pages 1427-1439, September.
    3. Erjia Yan & Ying Ding & Elin K. Jacob, 2012. "Overlaying communities and topics: an analysis on publication networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 499-513, February.
    4. Erjia Yan & Ying Ding, 2012. "Scholarly network similarities: How bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(7), pages 1313-1326, July.
    5. Erjia Yan & Ying Ding, 2012. "Scholarly network similarities: How bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(7), pages 1313-1326, July.
    6. Graeme Hirst, 1978. "Discipline impact factors: A method for determining core journal lists," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 29(4), pages 171-172, July.
    7. Staša Milojević & Cassidy R. Sugimoto & Erjia Yan & Ying Ding, 2011. "The cognitive structure of Library and Information Science: Analysis of article title words," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(10), pages 1933-1953, October.
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    Cited by:

    1. Ma, Jing & Abrams, Natalie F. & Porter, Alan L. & Zhu, Donghua & Farrell, Dorothy, 2019. "Identifying translational indicators and technology opportunities for nanomedical research using tech mining: The case of gold nanostructures," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 767-775.
    2. Erjia Yan & Chaojiang Wu & Min Song, 2018. "The funding factor: a cross-disciplinary examination of the association between research funding and citation impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 369-384, April.
    3. Yongjun Zhu & Min Song & Erjia Yan, 2016. "Identifying Liver Cancer and Its Relations with Diseases, Drugs, and Genes: A Literature-Based Approach," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-14, May.

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