IDEAS home Printed from https://ideas.repec.org/a/hin/complx/5785617.html
   My bibliography  Save this article

A Novel Procedure for Measuring Semantic Synergy

Author

Listed:
  • Yair Neuman
  • Yiftach Neuman
  • Yochai Cohen

Abstract

One interesting characteristic of some complex systems is the formation of macro level constructions perceived as having features that cannot be reduced to their micro level constituents. This characteristic is considered to be the expression of synergy where the joint action of the constituents produces unique features that are irreducible to the constituents isolated behavior or their simple composition. The synergy, characterizing complex systems, has been well acknowledged but difficult to conceptualize and quantify in the context of computing the emerging meaning of various linguistic and conceptual constructs. In this paper, we propose a novel measure/procedure for quantifying semantic synergy. This measure draws on a general idea of synergy as has been proposed in biology. We validate this measure by providing evidence for its ability to predict the semantic transparency of linguistic compounds (Experiment ) and the abstractness rating of nouns (Experiment ).

Suggested Citation

  • Yair Neuman & Yiftach Neuman & Yochai Cohen, 2017. "A Novel Procedure for Measuring Semantic Synergy," Complexity, Hindawi, vol. 2017, pages 1-8, January.
  • Handle: RePEc:hin:complx:5785617
    DOI: 10.1155/2017/5785617
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2017/5785617.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/8503/2017/5785617.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2017/5785617?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. Xu Han & Deyun Chen & Hailu Yang, 2019. "A Semantic Community Detection Algorithm Based on Quantizing Progress," Complexity, Hindawi, vol. 2019, pages 1-13, January.

    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:hin:complx:5785617. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.