IDEAS home Printed from https://ideas.repec.org/a/igg/jswis0/v12y2016i3p1-26.html
   My bibliography  Save this article

Selectivity-Based Keyword Extraction Method

Author

Listed:
  • Slobodan Beliga

    (Department of Informatics, University of Rijeka, Rijeka, Croatia)

  • Ana Meštrović

    (Department of Informatics, University of Rijeka, Rijeka, Croatia)

  • Sanda Martinčić-Ipšić

    (Department of Informatics, University of Rijeka, Rijeka, Croatia)

Abstract

In this work the authors propose a novel Selectivity-Based Keyword Extraction (SBKE) method, which extracts keywords from the source text represented as a network. The node selectivity value is calculated from a weighted network as the average weight distributed on the links of a single node and is used in the procedure of keyword candidate ranking and extraction. The authors show that selectivity-based keyword extraction slightly outperforms an extraction based on the standard centrality measures: in/out-degree, betweenness and closeness. Therefore, they include selectivity and its modification – generalized selectivity as node centrality measures in the SBKE method. Selectivity-based extraction does not require linguistic knowledge as it is derived purely from statistical and structural information of the network. The experimental results point out that selectivity-based keyword extraction has a great potential for the collection-oriented keyword extraction task.

Suggested Citation

  • Slobodan Beliga & Ana Meštrović & Sanda Martinčić-Ipšić, 2016. "Selectivity-Based Keyword Extraction Method," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 12(3), pages 1-26, July.
    • Handle: RePEc:igg:jswis0:v:12:y:2016:i:3:p:1-26
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSWIS.2016070101
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Wei Lu & Yong Huang & Yi Bu & Qikai Cheng, 2018. "Functional structure identification of scientific documents in computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 463-486, April.
    2. Garg, Muskan & Kumar, Mukesh, 2018. "The structure of word co-occurrence network for microblogs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 698-720.

    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:igg:jswis0:v:12:y:2016:i:3:p:1-26. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.