IDEAS home Printed from https://ideas.repec.org/a/bla/jamest/v47y1996i1p70-84.html
   My bibliography  Save this article

Stemming algorithms: A case study for detailed evaluation

Author

Listed:
  • David A. Hull

Abstract

The majority of information retrieval experiments are evaluated by measures such as average precision and average recall. Fundamental decisions about the superiority of one retrieval technique over another are made solely on the basis of these measures. We claim that average performance figures need to be validated with a careful statistical analysis and that there is a great deal of additional information that can be uncovered by looking closely at the results of individual queries. This article is a case study of stemming algorithms which describes a number of novel approaches to evaluation and demonstrates their value. © 1996 John Wiley & Sons, Inc.

Suggested Citation

  • David A. Hull, 1996. "Stemming algorithms: A case study for detailed evaluation," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 47(1), pages 70-84, January.
    • Handle: RePEc:bla:jamest:v:47:y:1996:i:1:p:70-84
    DOI: 10.1002/(SICI)1097-4571(199601)47:13.0.CO;2-#
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/(SICI)1097-4571(199601)47:13.0.CO;2-#
    Download Restriction: no
    File URL: https://libkey.io/10.1002/(SICI)1097-4571(199601)47:13.0.CO;2-#?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. Mohamed Ramzi Haddad & Hajer Baazaoui & Hemza Ficel, 2018. "A Scalable and Interactive Recommendation Model for Users’ Interests Prediction," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(05), pages 1335-1361, September.
    2. Ha, Sohee & Geum, Youngjung, 2022. "Identifying new innovative services using M&A data: An integrated approach of data-driven morphological analysis," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    3. Hana Lee & Young Yoon, 2018. "Engineering doc2vec for automatic classification of product descriptions on O2O applications," Electronic Commerce Research, Springer, vol. 18(3), pages 433-456, September.
    4. Hofmann, Peter & Keller, Robert & Urbach, Nils, 2019. "Inter-technology relationship networks: Arranging technologies through text mining," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 202-213.

    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:jamest:v:47:y:1996:i:1:p:70-84. 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.