IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i19p2502-d650711.html
   My bibliography  Save this article

Definition Extraction from Generic and Mathematical Domains with Deep Ensemble Learning

Author

Listed:
  • Natalia Vanetik

    (Software Engineering Department, Shamoon College of Engineering, Bialik 56, Beer Sheva 8434231, Israel
    These authors contributed equally to this work.)

  • Marina Litvak

    (Software Engineering Department, Shamoon College of Engineering, Bialik 56, Beer Sheva 8434231, Israel
    These authors contributed equally to this work.)

Abstract

Definitions are extremely important for efficient learning of new materials. In particular, mathematical definitions are necessary for understanding mathematics-related areas. Automated extraction of definitions could be very useful for automated indexing educational materials, building taxonomies of relevant concepts, and more. For definitions that are contained within a single sentence, this problem can be viewed as a binary classification of sentences into definitions and non-definitions. In this paper, we focus on automatic detection of one-sentence definitions in mathematical and general texts. We experiment with different classification models arranged in an ensemble and applied to a sentence representation containing syntactic and semantic information, to classify sentences. Our ensemble model is applied to the data adjusted with oversampling. Our experiments demonstrate the superiority of our approach over state-of-the-art methods in both general and mathematical domains.

Suggested Citation

  • Natalia Vanetik & Marina Litvak, 2021. "Definition Extraction from Generic and Mathematical Domains with Deep Ensemble Learning," Mathematics, MDPI, vol. 9(19), pages 1-18, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2502-:d:650711
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/19/2502/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/19/2502/
    Download Restriction: no
    ---><---

    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:gam:jmathe:v:9:y:2021:i:19:p:2502-:d:650711. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.