1. Joint predictiveness in inflectional paradigms
- Author
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Sarah Beniamine, Olivier Bonami, Laboratoire de Linguistique Formelle (LLF UMR7110), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)
- Subjects
060201 languages & linguistics ,Conditional entropy ,Linguistics and Language ,Theoretical computer science ,Quantitative morphology ,linguistics ,06 humanities and the arts ,Language and Linguistics ,030507 speech-language pathology & audiology ,03 medical and health sciences ,0602 languages and literature ,Inflection ,Entropy (information theory) ,[SHS.LANGUE]Humanities and Social Sciences/Linguistics ,0305 other medical science ,Algorithm ,Principal parts ,Filling Problem ,Mathematics - Abstract
International audience; This paper contributes to addressing the Paradigm Cell Filling Problem (PCFP) in inflectional paradigms, as defined by Ackerman et al. (2009). We define a method for extending the use of conditional entropy to address the PCFP to prediction based on multiple paradigm cells. We apply this method to French and European Portugese and show that, on average, knowledge of multiple paradigm cells is dramatically more predictive than knowledge of a single cell. Moreover, this new entropy measure proves useful in studying principal parts systems, which correspond to sets of predictors yielding a null entropy. Using a graded measure allows us to highlight the relevance of non-categorical or “good enough” principal parts systems.
- Published
- 2016