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Modelling the interaction of regularity and morphological structure: the case of Russian verb inflection

Conference Paper
Publication Date:
2019
abstract:
Modelling complex inflection systems, such as conjugation in Modern Greek, Italian or Russian, requires careful consideration of a number of factors, ranging from pervasive stem allomorphy to the identification of the appropriate inflection class and the inferential predictability of morpho-phonological processes. Descriptive approaches have taken different views on how to account for degrees of morphological (ir)regularity, while making different predictions about the way speakers process regular and irregular forms in highly-inflecting languages. In the present paper, we assess the psycholinguistic implications of two radically different approaches to the description of the Russian verb system: a more traditional approach dating back to Jakobson (1948), and a Words and Paradigm approach (Brown 1998). Based on recent fMRI evidence (Slioussar et al. 2014) and original results of a neural network simulation with recurrent self-organising maps (Ferro et al. 2011; Marzi et al. 2014; Pirrelli et al. 2015; Marzi et al. 2016), we suggest that both approaches are prima facie compatible with Russian data, while being in contrast with Pinker's claim that the regular-irregular distinction is an epiphenomenon of the storage-processing dichotomy in the human language faculty (Pinker & Ullman 2002). We argue that this evidence lends support to integrative models of the mental lexicon (Marzi & Pirrelli 2015), accounting for a graded interaction between regularity and morphological structure.
Iris type:
04.01 Contributo in Atti di convegno
Keywords:
Inflectional complexity; Russian verb system; perception of morphological structure; recurrent self-organising neural network
List of contributors:
Marzi, Claudia
Authors of the University:
MARZI CLAUDIA
Handle:
https://iris.cnr.it/handle/20.500.14243/386352
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http://drehu.linguist.univ-paris-diderot.fr/ismo-2019/?fichier=programme
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