IDEAS home Printed from https://ideas.repec.org/a/bla/jamist/v57y2006i11p1506-1518.html
   My bibliography  Save this article

Learning to classify documents according to genre

Author

Listed:
  • Aidan Finn
  • Nicholas Kushmerick

Abstract

Current document‐retrieval tools succeed in locating large numbers of documents relevant to a given query. While search results may be relevant according to the topic of the documents, it is more difficult to identify which of the relevant documents are most suitable for a particular user. Automatic genre analysis (i.e., the ability to distinguish documents according to style) would be a useful tool for identifying documents that are most suitable for a particular user. We investigate the use of machine learning for automatic genre classification. We introduce the idea of domain transfer—genre classifiers should be reusable across multiple topics—which does not arise in standard text classification. We investigate different features for building genre classifiers and their ability to transfer across multiple‐topic domains. We also show how different feature‐sets can be used in conjunction with each other to improve performance and reduce the number of documents that need to be labeled.

Suggested Citation

  • Aidan Finn & Nicholas Kushmerick, 2006. "Learning to classify documents according to genre," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(11), pages 1506-1518, September.
    • Handle: RePEc:bla:jamist:v:57:y:2006:i:11:p:1506-1518
    DOI: 10.1002/asi.20427
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asi.20427
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asi.20427?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. Rutherford, Brian A., 2013. "A genre-theoretic approach to financial reporting research," The British Accounting Review, Elsevier, vol. 45(4), pages 297-310.
    2. Jacques Savoy & Olena Zubaryeva, 2012. "Simple and efficient classification scheme based on specific vocabulary," Computational Management Science, Springer, vol. 9(3), pages 401-415, August.
    3. Ruchika Malhotra & Anjali Sharma, 2017. "Quantitative evaluation of web metrics for automatic genre classification of web pages," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1567-1579, November.

    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:57:y:2006:i:11:p:1506-1518. 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.