IDEAS home Printed from https://ideas.repec.org/a/bla/jinfst/v67y2016i11p2684-2696.html
   My bibliography  Save this article

Predicting the impact of scientific concepts using full-text features

Author

Listed:
  • Kathy McKeown
  • Hal Daume III
  • Snigdha Chaturvedi
  • John Paparrizos
  • Kapil Thadani
  • Pablo Barrio
  • Or Biran
  • Suvarna Bothe
  • Michael Collins
  • Kenneth R. Fleischmann
  • Luis Gravano
  • Rahul Jha
  • Ben King
  • Kevin McInerney
  • Taesun Moon
  • Arvind Neelakantan
  • Diarmuid O'Seaghdha
  • Dragomir Radev
  • Clay Templeton
  • Simone Teufel

Abstract

No abstract is available for this item.

Suggested Citation

  • Kathy McKeown & Hal Daume III & Snigdha Chaturvedi & John Paparrizos & Kapil Thadani & Pablo Barrio & Or Biran & Suvarna Bothe & Michael Collins & Kenneth R. Fleischmann & Luis Gravano & Rahul Jha & B, 2016. "Predicting the impact of scientific concepts using full-text features," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(11), pages 2684-2696, November.
  • Handle: RePEc:bla:jinfst:v:67:y:2016:i:11:p:2684-2696
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1002/asi.23612
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    Citations

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


    Cited by:

    1. Kun Sun & Haitao Liu & Wenxin Xiong, 2021. "The evolutionary pattern of language in scientific writings: A case study of Philosophical Transactions of Royal Society (1665–1869)," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1695-1724, February.
    2. Lu, Kun & Yang, Guancan & Wang, Xue, 2022. "Topics emerged in the biomedical field and their characteristics," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    3. Lu, Chao & Bu, Yi & Dong, Xianlei & Wang, Jie & Ding, Ying & Larivière, Vincent & Sugimoto, Cassidy R. & Paul, Logan & Zhang, Chengzhi, 2019. "Analyzing linguistic complexity and scientific impact," Journal of Informetrics, Elsevier, vol. 13(3), pages 817-829.
    4. Bikun Chen & Dannan Deng & Zhouyan Zhong & Chengzhi Zhang, 2020. "Exploring linguistic characteristics of highly browsed and downloaded academic articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1769-1790, March.
    5. Florian Kreuchauff & Vladimir Korzinov, 2017. "A patent search strategy based on machine learning for the emerging field of service robotics," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 743-772, May.
    6. Akella, Akhil Pandey & Alhoori, Hamed & Kondamudi, Pavan Ravikanth & Freeman, Cole & Zhou, Haiming, 2021. "Early indicators of scientific impact: Predicting citations with altmetrics," Journal of Informetrics, Elsevier, vol. 15(2).
    7. Kevin Heffernan & Simone Teufel, 2018. "Identifying problems and solutions in scientific text," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 1367-1382, August.
    8. Jorge A. V. Tohalino & Laura V. C. Quispe & Diego R. Amancio, 2021. "Analyzing the relationship between text features and grants productivity," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4255-4275, May.
    9. Katchanov, Yurij L. & Markova, Yulia V., 2022. "Dynamics of senses of new physics discourse: Co-keywords analysis," Journal of Informetrics, Elsevier, vol. 16(1).
    10. Gao, Qiang & Liang, Zhentao & Wang, Ping & Hou, Jingrui & Chen, Xiuxiu & Liu, Manman, 2021. "Potential index: Revealing the future impact of research topics based on current knowledge networks," Journal of Informetrics, Elsevier, vol. 15(3).
    11. Shahzad, Murtuza & Alhoori, Hamed & Freedman, Reva & Rahman, Shaikh Abdul, 2022. "Quantifying the online long-term interest in research," Journal of Informetrics, Elsevier, vol. 16(2).
    12. Toluwase Victor Asubiaro & Isola Ajiferuke, 2022. "Semantic similarity-based credit attribution on citation paths: a method for allocating residual citation to and investigating depth of influence of scientific communications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6257-6277, November.
    13. Chao Lu & Ying Ding & Chengzhi Zhang, 2017. "Understanding the impact change of a highly cited article: a content-based citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(2), pages 927-945, August.

    More about this item

    Statistics

    Access and download statistics

    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:bla:jinfst:v:67:y:2016:i:11:p:2684-2696. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.asis.org .

    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.