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- W4361994857 abstract "This chapter is devoted to a detailed description of the methodology that will be applied in this work to perform an analysis of Latin inflectional paradigms that satisfies the theoretical desiderata outlined in Chap. 1 . The applied method makes use of notions and procedures taken from information-theory (Shannon 1948), notably surprisal and entropy. In Sect. 2.1, a basic introduction to such information-theoretic notions is offered, pointing out the properties that make them useful to investigate topics related to implicative relations and the Paradigm Cell Filling Problem. The first proposal to use entropy for this purpose was outlined in Ackerman et al. (2009), whose procedure—aiming at an estimate of the degree of uncertainty associated with morphological realizations—is described in Sect. 2.2. However, the tools and algorithm that are used throughout this work are based on a similar but refined procedure (cf. Bonami 2014, Bonami and Boyé 2014, Beniamine 2018), that only requires a lexicon of inflected wordforms with no a priori morphological analysis. This method can be used not only to estimate the uncertainty in guessing the content of the paradigm cell of a lexeme knowing one inflected wordform, as explained in Sect. 2.3, but also given knowledge of multiple wordforms, as detailed in Sect. 2.4. The possible impact of additional information in making such tasks easier will be discussed in Sect. 2.5, where two possible variables are mentioned, namely i) the gender of a noun, and ii) the fact that a given lexeme is derivationally related to another one. In Sect. 2.6, the most important characteristics of the adopted methodology will be summarized, clarifying how they relate to the theoretical principles discussed in the first chapter." @default.
- W4361994857 created "2023-04-05" @default.
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- W4361994857 date "2023-01-01" @default.
- W4361994857 modified "2023-10-18" @default.
- W4361994857 title "The Method" @default.
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- W4361994857 doi "https://doi.org/10.1007/978-3-031-24844-3_2" @default.
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