Abstract
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.