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Among the German negative-conditional connectors in the range of consequens markers there are the prototypical cases sonst and ansonsten. Morphological alternatives (sonsten and ansonst) are rarely mentioned in contemporary grammars and dictionaries but they actually occur with considerable frequency. The four connectors are used in two functions: as a conjunctional adverb which can occupy various positions within the sentence or as a specific kind of subordinating conjunction (Postponierer). The large IDS corpora allow us to reveal specific distributions of the lexemes and of their different ways of use. Comparing the frequencies and the distributions can indicate to which extent the phenomena are part of the standard language. The paper will report on the results and demonstrate how the findings can be deduced from the corpora. It will draw conclusions for assessing the acceptability of the variants and the extent to which they can be considered standard language additionally testing statistical instruments to visualise and calculate the variance of phenomena as association plots and DPnorm.
Introduction
(2012)
The variation of the strong genitive marker of the singular noun has been treated by diverse accounts. Still there is a consensus that it is to a large extent systematic but can be approached appropriately only if many heterogeneous factors are taken into account. Over thirty variables influencing this variation have been proposed. However, it is actually unclear how effective they can be, and above all, how they interact. In this paper, the potential influencing variables are evaluated statistically in a machine learning approach and modelled in decision trees in order to predict the genitive marking variants. Working with decision trees based exclusively on statistically significant data enables us to determine what combination of factors is decisive in the choice of a marking variant of a given noun. Consequently the variation factors can be assessed with respect to their explanatory power for corpus data and put in a hierarchized order.