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A Novel Procedure for Measuring Semantic Synergy

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  • 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
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    1. Anonymous, 2014. "Introduction to the Issue," Journal of Wine Economics, Cambridge University Press, vol. 9(1), pages 1-2, May.
    2. Anonymous, 2014. "Introduction to the Issue," Journal of Wine Economics, Cambridge University Press, vol. 9(2), pages 109-110, August.
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    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.

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