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This paper demonstrates systematic cross-linguistic differences in the electrophysiological correlates of conflicts between form and meaning (“semantic reversal anomalies”). These engender P600 effects in English and Dutch (e.g. Kolk et al., 2003, Kuperberg et al., 2003), but a biphasic N400 – late positivity pattern in German (Schlesewsky and Bornkessel-Schlesewsky, 2009), and monophasic N400 effects in Turkish (Experiment 1) and Mandarin Chinese (Experiment 2). Experiment 3 revealed that, in Icelandic, semantic reversal anomalies show the English pattern with verbs requiring a position-based identification of argument roles, but the German pattern with verbs requiring a case-based identification of argument roles. The overall pattern of results reveals two separate dimensions of cross-linguistic variation: (i) the presence vs. absence of an N400, which we attribute to cross-linguistic differences with regard to the sequence-dependence of the form-to-meaning mapping and (ii) the presence vs. absence of a late positivity, which we interpret as an instance of a categorisation-related late P300, and which is observable when the language under consideration allows for a binary well-formedness categorisation of reversal anomalies. We conclude that, rather than reflecting linguistic domains such as syntax and semantics, the late positivity vs. N400 distinction is better understood in terms of the strategies that serve to optimise the form-to-meaning mapping in a given language.
In this paper, we will present a first attempt to classify commonly confused words in German by consulting their communicative functions in corpora. Although the use of so-called paronyms causes frequent uncertainties due to similarities in spelling, sound and semantics, up until now the phenomenon has attracted little attention either from the perspective of corpus linguistics or from cognitive linguistics. Existing investigations rely on structuralist models, which do not account for empirical evidence. Still, they have developed an elaborate model based on formal criteria, primarily on word formation (cf. Lăzărescu 1999). Looking from a corpus perspective, such classifications are incompatible with language in use and cognitive elements of misuse.
This article sketches first lexicological insights into a classification model as derived from semantic analyses of written communication. Firstly, a brief description of the project will be provided. Secondly, corpus-assisted paronym detection will be focused. Thirdly, in the main section the paper concerns the description of the datasets for paronym classification and the classification procedures. As a work in progress, new insights will continually be extended once spoken and CMC data are added to the investigations.