IDEAS home Printed from https://ideas.repec.org/a/bla/jamist/v59y2008i7p1026-1040.html
   My bibliography  Save this article

Domain‐independent automatic keyphrase indexing with small training sets

Author

Listed:
  • Olena Medelyan
  • Ian H. Witten

Abstract

Keyphrases are widely used in both physical and digital libraries as a brief, but precise, summary of documents. They help organize material based on content, provide thematic access, represent search results, and assist with navigation. Manual assignment is expensive because trained human indexers must reach an understanding of the document and select appropriate descriptors according to defined cataloging rules. We propose a new method that enhances automatic keyphrase extraction by using semantic information about terms and phrases gleaned from a domain‐specific thesaurus. The key advantage of the new approach is that it performs well with very little training data. We evaluate it on a large set of manually indexed documents in the domain of agriculture, compare its consistency with a group of six professional indexers, and explore its performance on smaller collections of documents in other domains and of French and Spanish documents.

Suggested Citation

  • Olena Medelyan & Ian H. Witten, 2008. "Domain‐independent automatic keyphrase indexing with small training sets," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(7), pages 1026-1040, May.
  • Handle: RePEc:bla:jamist:v:59:y:2008:i:7:p:1026-1040
    DOI: 10.1002/asi.20790
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asi.20790
    Download Restriction: no

    File URL: https://libkey.io/10.1002/asi.20790?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
    ---><---

    Citations

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


    Cited by:

    1. Sidhamed Elandaloussi & Pascale Zarate & Noria Taghezout, 2021. "A Text Mining Approach Agent-Based DSS for IT Infrastructure Maintenance," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 13(3), pages 1-21, July.
    2. Song, Min & Kim, Erin Hea-Jin & Kim, Ha Jin, 2015. "Exploring author name disambiguation on PubMed-scale," Journal of Informetrics, Elsevier, vol. 9(4), pages 924-941.

    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:jamist:v:59:y:2008:i:7:p:1026-1040. 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.