IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v126y2021i8d10.1007_s11192-021-04028-4.html
   My bibliography  Save this article

Text representation model of scientific papers based on fusing multi-viewpoint information and its quality assessment

Author

Listed:
  • Yonghe Lu

    (Sun Yat-sen University)

  • Jiayi Luo

    (Sun Yat-sen University)

  • Ying Xiao

    (Sun Yat-sen University)

  • Hou Zhu

    (Sun Yat-sen University)

Abstract

Text representation is the preliminary work for in-depth analysis and mining of information in scientific papers. It directly affects the effects of downstream tasks such as, scientific papers classification, clustering, and similarity calculation. However, recent researches mainly considered citation network and partial structural information, which is insufficient when representing scientific papers. Therefore, in order to improve the performance of text representation model, this paper proposed MV-HATrans, a text representation model that combines multi-viewpoint information, such as the semantic information of knowledge graph and structural information. This model extracts word information from three aspects, including contextual content, part of speech, and word meaning of WordNet. Based on combination of hierarchical attention mechanism and transformer, the model achieves the full text representation of scientific papers. Finally, this paper uses the binary experimental dataset AAPR, which indicates whether scientific papers are accepted or not, and applies the proposed model of text representation to achieve the goal of automatic quality assessment. Results show that in the quality classification of scientific papers, adopting part-of-speech information and semantic information based on WordNet definitions can effectively achieve the accuracy of prediction as 70.14%. Among all the structural modules, authors and abstracts contributes the most to the quality classification of scientific papers, especially authors as 9.51%.

Suggested Citation

  • Yonghe Lu & Jiayi Luo & Ying Xiao & Hou Zhu, 2021. "Text representation model of scientific papers based on fusing multi-viewpoint information and its quality assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6937-6963, August.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:8:d:10.1007_s11192-021-04028-4
    DOI: 10.1007/s11192-021-04028-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-021-04028-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-021-04028-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yves Fassin, 2018. "A new qualitative rating system for scientific publications and a fame index for academics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(11), pages 1396-1399, November.
    2. Negin Salimi, 2017. "Quality assessment of scientific outputs using the BWM," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 195-213, July.
    3. Shu Zhao & Dong Zhang & Zhen Duan & Jie Chen & Yan-ping Zhang & Jie Tang, 2018. "A novel classification method for paper-reviewer recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(3), pages 1293-1313, June.
    4. Titipat Achakulvisut & Daniel E Acuna & Tulakan Ruangrong & Konrad Kording, 2016. "Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-11, July.
    5. Kevin Heffernan & Simone Teufel, 2018. "Identifying problems and solutions in scientific text," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 1367-1382, August.
    6. Fen Zhao & Yi Zhang & Jianguo Lu & Ofer Shai, 2019. "Measuring academic influence using heterogeneous author-citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 1119-1140, March.
    7. Vahe Tshitoyan & John Dagdelen & Leigh Weston & Alexander Dunn & Ziqin Rong & Olga Kononova & Kristin A. Persson & Gerbrand Ceder & Anubhav Jain, 2019. "Unsupervised word embeddings capture latent knowledge from materials science literature," Nature, Nature, vol. 571(7763), pages 95-98, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yiqin Lv & Zheng Xie & Xiaojing Zuo & Yiping Song, 2022. "A multi-view method of scientific paper classification via heterogeneous graph embeddings," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4847-4872, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yi Zhang & Fen Zhao & Jianguo Lu, 2019. "P2V: large-scale academic paper embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 399-432, October.
    2. Ananthan Nambiar & Tobias Rubel & James McCaull & Jon deVries & Mark Bedau, 2021. "Dropping diversity of products of large US firms: Models and measures," Papers 2110.08367, arXiv.org.
    3. Salimi, Negin & Rezaei, Jafar, 2018. "Evaluating firms’ R&D performance using best worst method," Evaluation and Program Planning, Elsevier, vol. 66(C), pages 147-155.
    4. Željko Stević & Irena Đalić & Dragan Pamučar & Zdravko Nunić & Slavko Vesković & Marko Vasiljević & Ilija Tanackov, 2019. "A new hybrid model for quality assessment of scientific conferences based on Rough BWM and SERVQUAL," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 1-30, April.
    5. Jason Youn & Navneet Rai & Ilias Tagkopoulos, 2022. "Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    6. Gangan Prathap, 2019. "Expected, observed and relative paper scores from heterogeneous author-paper-citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 1275-1279, May.
    7. Wu, Lingfei & Kittur, Aniket & Youn, Hyejin & Milojević, Staša & Leahey, Erin & Fiore, Stephen M. & Ahn, Yong-Yeol, 2022. "Metrics and mechanisms: Measuring the unmeasurable in the science of science," Journal of Informetrics, Elsevier, vol. 16(2).
    8. Gordana Ispirova & Tome Eftimov & Barbara Koroušić Seljak, 2020. "P-NUT: Predicting NUTrient Content from Short Text Descriptions," Mathematics, MDPI, vol. 8(10), pages 1-21, October.
    9. Ana Teresa Santos & Sandro Mendonça, 2022. "Do papers (really) match journals’ “aims and scope”? A computational assessment of innovation studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7449-7470, December.
    10. Lin, Yiling & Evans, James A. & Wu, Lingfei, 2022. "New directions in science emerge from disconnection and discord," Journal of Informetrics, Elsevier, vol. 16(1).
    11. Zongrui Pei & Junqi Yin & Peter K. Liaw & Dierk Raabe, 2023. "Toward the design of ultrahigh-entropy alloys via mining six million texts," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    12. Shaoshuo Li & Baixing Chen & Hao Chen & Zhen Hua & Yang Shao & Heng Yin & Jianwei Wang, 2021. "Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-18, September.
    13. Jeong, Yoo Kyung & Xie, Qing & Yan, Erjia & Song, Min, 2020. "Examining drug and side effect relation using author–entity pair bipartite networks," Journal of Informetrics, Elsevier, vol. 14(1).
    14. John Dagdelen & Alexander Dunn & Sanghoon Lee & Nicholas Walker & Andrew S. Rosen & Gerbrand Ceder & Kristin A. Persson & Anubhav Jain, 2024. "Structured information extraction from scientific text with large language models," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    15. Xiaoyu Liu & Xuefeng Wang & Donghua Zhu, 2022. "Reviewer recommendation method for scientific research proposals: a case for NSFC," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3343-3366, June.
    16. Krzysztof Przystupa & Mykola Beshley & Olena Hordiichuk-Bublivska & Marian Kyryk & Halyna Beshley & Julia Pyrih & Jarosław Selech, 2021. "Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems," Energies, MDPI, vol. 14(8), pages 1-24, April.
    17. Geerten Van de Kaa & Daniel Scholten & Jafar Rezaei & Christine Milchram, 2017. "The Battle between Battery and Fuel Cell Powered Electric Vehicles: A BWM Approach," Energies, MDPI, vol. 10(11), pages 1-13, October.
    18. Cristina López-Duarte & Marta M. Vidal-Suárez & Belén González-Díaz, 2019. "Cross-national distance and international business: an analysis of the most influential recent models," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 173-208, October.
    19. Tehmina Amjad & Yusra Rehmat & Ali Daud & Rabeeh Ayaz Abbasi, 2020. "Scientific impact of an author and role of self-citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 915-932, February.
    20. Mališa Žižović & Dragan Pamučar & Goran Ćirović & Miodrag M. Žižović & Boža D. Miljković, 2020. "A Model for Determining Weight Coefficients by Forming a Non-Decreasing Series at Criteria Significance Levels (NDSL)," Mathematics, MDPI, vol. 8(5), pages 1-18, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:126:y:2021:i:8:d:10.1007_s11192-021-04028-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.