Abstract
We approach the problem of interpretability for fuzzy linguistic descriptions of data from a natural language generation perspective. For this, first we review the current state of linguistic descriptions of data and their use contexts as a standalone tool and as part of a natural language generation system. Then, we discuss the standard approach to interpretability for linguistic descriptions and introduce our complementary proposal, which describes the elements from linguistic descriptions of data that can influence and improve the interpretability of automatically generated texts (such as fuzzy properties, quantifiers, and truth degrees), when linguistic descriptions are used to determine relevant content within a text generation system.
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Acknowledgments
This work has been funded by TIN2014-56633-C3-1-R and TIN2014-56633-C3-3-R projects from the Spanish “Ministerio de Economía y Competitividad” and by the “Consellería de Cultura, Educación e Ordenación Universitaria” (accreditation 2016–2019, ED431G/08) and the European Regional Development Fund (ERDF). A. Ramos-Soto is funded by the “Consellería de Cultura, Educación e Ordenación Universitaria” (under the Postdoctoral Fellowship accreditation ED481B 2017/030). M. Pereira-Fariña is funded by the “Consellería de Cultura, Educación e Ordenación Universitaria” (under the Postdoctoral Fellowship accreditation ED481B 2016/048-0).
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Ramos-Soto, A., Pereira-Fariña, M. (2018). Reinterpreting Interpretability for Fuzzy Linguistic Descriptions of Data. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 853. Springer, Cham. https://doi.org/10.1007/978-3-319-91473-2_4
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DOI: https://doi.org/10.1007/978-3-319-91473-2_4
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