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This paper focuses on easily confused items (so-called paronyms) in German in terms of their general, technical or academic contextual uses. It outlines the semantic discrepancies between contextual usages of pairs such as Methode/Methodologie/Methodik and unehelich/nichtehelich/außerehelich depending on their linguistic registers and varieties. While previous studies lack empirical evidence and primarily operate with morphological criteria (cf. Lăzărescu 1999) the descriptions here derive from corpus-based examinations of general written and of technical discourse. It is shown that causes of lexical confusion arise from formal, phonetic resemblances or semantic similarities, regular co-occurrence, incorrect morphological analogies and political governance of language. Context, knowledge, associations and experience determine the choice of lexical terms. Speakers need to apply linguistic and extra-linguistic principles in order to create adequate contexts. With the help of paronym examples and corpus data, these will be elucidated in more detail.
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.