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Der Beitrag befasst sich zunächst mit der Satzklammer des Deutschen aus der Perspektive der Informationsverteilung. Nachdem gezeigt ist, dass sie als Informationsklammer fungiert, wird ihre Interaktion mit den Teilen gespaltener Nominalphrasen untersucht. Dabei zeigen sich zwei interessante Befunde:
• die Satzklammer und die NP-Teile unterstützen sich bei der Informationsklammerbildung; insbesondere können die Spalt-NP-Teile Akzent tragen;
• die Spalt-NP-Teile können alleine die Rolle einer Informationsklammer spielen, wodurch eine Topikalisierung des Partizips II möglich wird.
The paper at hand discusses productivity in German compound formation – as a case of morphological variation – from a lexeme-based synchronic perspective. In particular, we focus on groups of compounds with semantically closely related head words, e.g., compounds denoting colors.
Our approach is characterized by a qualitative as well as a quantitative perspective on productivity. Taking the properties of the head lexeme as a starting point and applying corpus-based statistical methods, we try to gain new insights into compound formation, especially into potential factors which govern their productivity. In a first step, we determine the productivity of compounds on the basis of current productivity measures and data from a large corpus of German. In a second step, we try to systematically explain observable differences in productivity.
The approach presented here is one of the first attempts to apply the concept of productivity, which has been predominantly used in the domain of derivation, to compounding. Since compounding is a dominant factor for the expansion of the German lexicon, we assume that our investigation also sheds an important light on the dynamics of the lexicon.
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.
From Open Source to Open Information. Collaborative Methods in Creating XML-based Markup Languages
(2000)
The administration of electronic publication in the Information Era congregates old and new problems, especially those related with Information Retrieval and Automatic Knowledge Extraction. This article presents an Information Retrieval System that uses Natural Language Processing and Ontology to index collection’s texts. We describe a system that constructs a domain specific ontology, starting from the syntactic and semantic analyses of the texts that compose the collection. First the texts are tokenized, then a robust syntactic analysis is made, subsequently the semantic analysis is accomplished in conformity with a metalanguage of knowledge representation, based on a basic ontology composed of 47 classes. The ontology, automatically extracted, generates richer domain specific knowledge. It propitiates, through its semantic net, the right conditions for the user to find with larger efficiency and agility the terms adapted for the consultation to the texts. A prototype of this system was built and used for the indexation of a collection of 221 electronic texts of Information Science written in Portuguese from Brazil. Instead of being based in statistical theories, we propose a robust Information Retrieval System that uses cognitive theories, allowing a larger efficiency in the answer to the users queries.