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The present paper reports the first results of the compilation and annotation of a blog corpus for German. The main aim of the project is the representation of the blog discourse structure and relations between its elements (blog posts, comments) and participants (bloggers, commentators). The data included in the corpus were manually collected from the scientific blog portal SciLogs. The feature catalogue for the corpus annotation includes three types of information which is directly or indirectly provided in the blog or can be construed by means of statistical analysis or computational tools. At this point, only directly available information (e.g. title of the blog post, name of the blogger etc.) has been annotated. We believe, our blog corpus can be of interest for the general study of blog structure or related research questions as well as for the development of NLP methods and techniques (e.g. for authorship detection).
From Open Source to Open Information. Collaborative Methods in Creating XML-based Markup Languages
(2000)
Wissenschaftlich basierte allgemeine Wörterbücher des Deutschen werden heute meist korpusbasiert erarbeitet, d. h. die in ihnen beschriebene Sprache wird vor der lexikografischen Beschreibung empirisch erforscht. Diese Korpora sind allerdings, wie die großen linguistischen Textsammlungen zum Deutschen allgemein, durch Zeitungstexte dominiert. Daher beruhen die in Wörterbüchern beschriebenen Kollokationen und typischen Verwendungskontexte zumindest teilweise auf dieser Textsorte. Wir untersuchen in unserem Beitrag anhand einer Fallstudie zu Mann und Frau, wie stark sich die Beschreibung solcher Kollokationssets ändern würde, wenn als Korpusgrundlage nicht Zeitungen, sondern Publikumszeitschriften oder belletristische Texte herangezogen würden und wie unterschiedlich demnach Geschlechterstereotype dargestellt würden. Damit diskutieren wir auch die Frage, ob Zeitungstexte in diesem Fall ein adäquates und vielseitiges Abbild des Gebrauchsstandards zeigen. Auf einer allgemeineren Ebene wird dadurch ein grundlegendes Problem korpuslinguistischer Forschungsarbeiten tangiert, nämlich die Frage, inwieweit durch Korpora überhaupt ein ‚objektives‘ Bild der sprachlichen Wirklichkeit gezeichnet werden kann.
Researchers in many disciplines, sometimes working in close cooperation, have been concerned with modeling textual data in order to account for texts as the prime information unit of written communication. The list of disciplines includes computer science and linguistics as well as more specialized disciplines like computational linguistics and text technology. What many of these efforts have in common is the aim to model textual data by means of abstract data types or data structures that support at least the semi-automatic processing of texts in any area of written communication.
Discourse segmentation is the division of a text into minimal discourse segments, which form the leaves in the trees that are used to represent discourse structures. A definition of elementary discourse segments in German is provided by adapting widely used segmentation principles for English minimal units, while considering punctuation, morphology, sytax, and aspects of the logical document structure of a complex text type, namely scientific articles. The algorithm and implementation of a discourse segmenter based on these principles is presented, as well an evaluation of test runs.
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.