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International Conference on Scalable Uncertainty Management

SUM 2016: Scalable Uncertainty Management pp 345–351Cite as

A Measure of Referential Success Based on Alpha-Cuts

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9858))

Abstract

In this paper we propose a measure of the referential success of a referring expression, defined by a collection of fuzzy properties, with respect to a certain object. The measure yields the degree to which the object is univocally identified by the referring expression among a collection of objects in a certain context. We consider the alpha-cuts of the fuzzy subset of objects that satisfy the referring expression as crisp versions of the problem, and we obtain the final measure by measuring the subset of levels in [0,1] where the referring expression has referential success in the crisp sense.

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References

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Acknowledgments

This work has been partially supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (FEDER) under project TIN2014-58227-P.

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Authors and Affiliations

  1. Department of Computer Science and A.I., University of Granada, 18071, Granada, Spain

    Nicolás Marín, Gustavo Rivas-Gervilla & Daniel Sánchez

Authors
  1. Nicolás Marín
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  2. Gustavo Rivas-Gervilla
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  3. Daniel Sánchez
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Corresponding author

Correspondence to Daniel Sánchez .

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Editors and Affiliations

  1. Cardiff University , Cardiff, United Kingdom

    Steven Schockaert

  2. Télécom ParisTech , Paris, Paris, France

    Pierre Senellart

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© 2016 Springer International Publishing Switzerland

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Marín, N., Rivas-Gervilla, G., Sánchez, D. (2016). A Measure of Referential Success Based on Alpha-Cuts. In: Schockaert, S., Senellart, P. (eds) Scalable Uncertainty Management. SUM 2016. Lecture Notes in Computer Science(), vol 9858. Springer, Cham. https://doi.org/10.1007/978-3-319-45856-4_25

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  • DOI: https://doi.org/10.1007/978-3-319-45856-4_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45855-7

  • Online ISBN: 978-3-319-45856-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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