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Fuzzy sets across the natural language generation pipeline

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Abstract

We explore the implications of using fuzzy techniques (mainly those commonly used in the linguistic description/summarization of data discipline) from a natural language generation perspective. For this, we provide an extensive discussion of some general convergence points and an exploration of the relationship between the different tasks involved in the standard natural language generation system pipeline architecture and the most common fuzzy approaches used in linguistic summarization/description of data, such as fuzzy quantified statements, evaluation criteria or aggregation operators. Each individual discussion is illustrated with a related use case. Recent work made in the context of cross-fertilization of both research fields is also referenced.

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Notes

  1. This technique is commonly known in NLG as “corpus” analysis and is performed systematically in the early conception stages of an NLG system [28].

  2. Note that we are using actual natural language expressions to illustrate lexicalization, but this task actually involves creating new sets of structures which define an intermediate syntax between the original content messages and the actual text [27].

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Authors and Affiliations

  1. Centro Singular de Investigación en Tecnoloxías da Información (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain

    A. Ramos-Soto, A. Bugarín & S. Barro

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  1. A. Ramos-Soto
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  2. A. Bugarín
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  3. S. Barro
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Correspondence to A. Ramos-Soto.

Additional information

This work was supported by the Spanish Ministry for Economy and Competitiveness (Grant TIN2014-56633-C3-1-R) and by the European Regional Development Fund (ERDF/FEDER) and the Galician Ministry of Education (Grants GRC2014/030 and CN2012/151). Alejandro Ramos Soto (A. Ramos-Soto) is supported by the Spanish Ministry for Economy and Competitiveness (FPI Fellowship Program) under Grant BES-2012-051878.

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Ramos-Soto, A., Bugarín, A. & Barro, S. Fuzzy sets across the natural language generation pipeline. Prog Artif Intell 5, 261–276 (2016). https://doi.org/10.1007/s13748-016-0097-x

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