49 Derivational networks in European languages: A cross-linguistic perspective
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AutorKörtvélyessy, Lívia; Bagasheva, Alexandra; Štekauer, Pavol; Genči, Ján; Valera Hernández, Salvador
Derivational NetworkSemantic CategoriesTypology
Körtvélyessy, Lívia, Bagasheva, Alexandra, Štekauer, Pavol, Valera, Salvador and Genči, Ján. 2020. "49 Derivational networks in European languages: A cross-linguistic perspective". Derivational Networks Across Languages, edited by Lívia Körtvélyessy, Alexandra Bagasheva and Pavol Štekauer, Berlin, Boston: De Gruyter Mouton, pp. 485-608. https://doi.org/10.1515/9783110686630-049
PatrocinadorThis article has been supported by the Spanish State Research Agency (SRA, Ministry of Economy and Enterprise) and European Regional Development Fund (ERDF) (Ref. FFI2017-89665-P).
In this final chapter, these preceding chapters and the 1,200 derivational networks on which they are based serve as an important and rich source of data and observations for drawing relevant cross-linguistic conclusions on the similarities and differences among the languages, as well as those language genera and/or languages that are sufficiently represented in our sample. In particular, we examine and compare the maximum derivational networks, saturation values, consistency of derivations at the language level and at the genera level, correlations between saturation values and the paradigmatic capacity, maximum and average numbers of orders of derivation, numbers of derivatives, correlations between semantic categories and orders of derivation, semantic categories with blocking effects, typical combinations of semantic categories, multiple occurrences of semantic categories, reversibility of semantic categories and the reasons for structurally poor derivational networks. The data are evaluated in terms of word-classes and orders of derivation, with a special focus on the role of genera and/or families, morphological types and the nature of the word-formation systems of individual languages. It is hypothesized that each of these five factors has an impact on (the possibility of) the generalization of our data.