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In dem vorliegende Beitrag haben wir uns zum Anlass genommen, die Rolle der Architektur für Interaktion grundsätzlich zu überdenken und systematisch anzugehen. Daraus ist der folgende Sammelband entstanden. Der Beitrag ergänzt die im Beitrag von Hausendorf/Schmitt (i.d.Bd.) entwickelte Perspektive auf ‘Interaktionsarchitektur’ und ‘Sozialtopografie’ um eine textlinguistische Perspektive: Die für die Interaktionsarchitekturanalyse zentralen ‘interaktionsarchitektonischen Implikationen’ lassen sich in ihrer Charakteristik weiter bestimmen, wenn man sie vor dem Hintergrund der für die Textanalyse zentralen ‘Lesbarkeitshinweise’ als Benutzbarkeitshinweise profiliert.
This paper shows how experimental methods can advance syntactic description and syntactic theory. The empirical domain is the order of verbs in German verb clusters containing a modal verb in the perfect. Such clusters are special insofar as prescriptive grammar requires the finite verb to appear in cluster-initial instead of cluster-final position (e.g., hat lesen müssen 'has read must' instead of lesen müssen hat 'read must has'). Contrary to this requirement, experiments show that native speakers accept the auxiliary also in later positions as long as it precedes the modal verb. The acceptability data are corroborated by corpus data and experimental data from language production. The relevance of the experimental data for syntactic theory are discussed.
Knowledge in textual form is always presented as visually and hierarchically structured units of text, which is particularly true in the case of academic texts. One research hypothesis of the ongoing project Knowledge ordering in texts - text structure and structure visualisations as sources of natural ontologies1 is that the textual structure of academic texts effectively mirrors essential parts of the knowledge structure that is built up in the text. The structuring of a modern dissertation thesis (e.g. in the form of an automatically generated table of contents - toes), for example, represents a compromise between requirements of the text type and the methodological and conceptual structure of its subject-matter. The aim of the project is to examine how visual-hierarchical structuring systems are constructed, how knowledge structures are encoded in them, and how they can be exploited to automatically derive ontological knowledge for navigation, archiving, or search tasks. The idea to extract domain concepts and semantic relations mainly from the structural and linguistic information gathered from tables of contents represents a novel approach to ontology learning.