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Assessing theory with practice: an evaluation of two aspectual-semantic classification models of gerundive nominalizations

Lauren Fonteyn

Abstract

This study present a corpus-based comparison of two aspectual-sematic classification models proposed in theoretical literature (unidimensional vs. bidimensional) by applying them to a set of nominal and verbal gerunds from the Modern English period. It (i) summarises the differences between unidimensional and bidimensional classification models and (ii) the potential problems associated with them. Despite the difficulties of studying semantic aspect in Present-day as well as historical data, this study will argue that, (iii) at least for deverbal nominalization patterns, it is possible to take a bidimensional approach and maintain a clear distinction between, on the one hand, aspect features of the nominalized situation (stativity/dynamicity, durativity/punctuality, and telicity/atelicity), and temporal boundedness of that situation. The question of which semantic classification model to use, then, is not so much one of which one is practically feasible in a corpus analysis, but rather which one is best suited to describe the attested variation. In order to determine the best model (in terms of parsimony and descriptive accuracy), (iv) the models were compared by means of ‘akaike weights’. To describe the variation between nominal and verbal gerunds in Early and Late Modern English, the bidimensional model outperformed the unidimensional one, showing that (v) the aspectual-semantic distinctions between Modern English nominal and verbal gerunds are a matter of both aspect and temporal boundedness.

Acknowledgement

I am grateful to Liesbet Heyvaert, Hendrik De Smet, Charlotte Maekelberghe, and Folgert Karsdorp for their valuable comments on earlier versions of this paper. I would also like to thank one anonymous reviewer, who had some particularly insightful comments on the first submitted version of this article.

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Published Online: 2018-03-21
Published in Print: 2020-10-25

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