IDEAS home Printed from
   My bibliography  Save this paper

Machine Learning-Based Search for Similar Unstructured Text Entries Using Only a Few Positive Samples with Ranking by Similarity


  • Jan Zizka

    () (Department of Informatics, Faculty of Business and Economics, Mendel University in Brno)

  • Frantisek Darena

    () (Department of Informatics, Faculty of Business and Economics, Mendel University in Brno)

  • Arnost Svoboda

    () (Department of Applied Mathematics and Computer Science, Faculty of Economics and Administration, Masaryk University)


This research was inspired by the procedures that are used by human bibliographic searchers: Given some textual, only 'positive' (interesting) examples, coming from one category find the most similar ones that belong to a relevant topic. The problem of categorization of unlabeled relevant and irrelevant textual documents is here solved by using a small subset of relevant available patterns labeled manually. Unlabeled text items are compared with such labeled patterns. The unlabeled samples are then ranked according their degree of similarity with the patterns. At the top of the rank, there are the most similar (relevant) items. Entries receding from the rank top represent less and less similar entries. This simple method, aimed at processing large volumes of text entries, provides practically acceptable filtering results from the accuracy point of view and users can avoid the demanding task of labeling too many training examples to be able to apply a chosen classifier. The ranking-based approach provides results that can be further used for the following text-item processing where the number of irrelevant items is already not so high as it is usually typical for, for example, only the raw browsing results provided by Internet search engines. Even if this relatively simple automatic search is not errorless, it can help process particularly very large textual unstructured data volumes. Such an approach can help also in the economics area, for example, to automatically categorize written opinions of customers (as is collecting via the Internet), process network-based discussion groups, and so like.

Suggested Citation

  • Jan Zizka & Frantisek Darena & Arnost Svoboda, 2011. "Machine Learning-Based Search for Similar Unstructured Text Entries Using Only a Few Positive Samples with Ranking by Similarity," MENDELU Working Papers in Business and Economics 2010-03, Mendel University in Brno, Faculty of Business and Economics.
  • Handle: RePEc:men:wpaper:03_2010

    Download full text from publisher

    File URL:
    File Function: Full text
    Download Restriction: no

    More about this item


    unlabeled text documents; one-class categorization; text similarity; ranking by similarity; pattern recognition; machine learning; natural language processing;

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:men:wpaper:03_2010. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Luděk Kouba). General contact details of provider: .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.