Standarddeutsch messen? Frequenz und Varianz negativ-konditionaler Konnektoren
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...
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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. ; Prototypickými případy těchto konektorů jsou v němčině slova sonst (jinak) a ansonsten (jinak). Jejich morfologické alternativy sonsten a ansonst se v současných gramatikách a slovnících téměř nezmiňují, avšak jejich frekvence v textech není zanedbatelná. Všechny tyto čtyři konektory se užívají ve dvou funkcích: jako spojkové adverbium, jež může ve větě zaujímat různé pozice, nebo jako specifický druh subordinační konjunkce (postponent). Velké korpusy IDS umožňují odhalovat specifickou distribuci těchto lexémů a různých způsobů jejich užívání. Srovnávání frekvence a distribuce může indikovat, do jaké míry jsou tyto jevy součástí standardního jazyka. Tato studie přináší výsledky tohoto srovnávání a ukazuje, jak lze korpusové nálezy interpretovat. Prostřednictvím testování statistických nástrojů se vyslovuje k akceptabilitě variant a míře, v níž mohou náležet standardnímu spisovnému jazyku
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Korpusbasierte Exploration der Variation der nominalen Genitivmarkierung
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...
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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.
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Korpusbasierte Exploration der Variation der nominalen Genitivmarkierung
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...
mehr
Volltext:
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Zitierfähiger Link:
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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.
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