Korpuslinguistik
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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).
In this paper, we present first results of training a classifier for discriminating Russian texts into different levels of difficulty. For the classification we considered both surface-oriented features adopted from readability assessments and more linguistically informed, positional features to classify texts into two levels of difficulty. This text classification is the main focus of our Levelled Study Corpus of Russian (LeStCoR), in which we aim to build a corpus adapted for language learning purposes – selecting simpler texts for beginner second language learners and more complex texts for advanced learners. The most discriminative feature in our pilot study was a lexical feature that approximates accessibility of the vocabulary by the second language learner in terms of the proportion of familiar words in the texts. The best feature setting achieved an accuracy of 0.91 on a pilot corpus of 209 texts.
Präposition-Substantiv-Verbindungen mit rekurrentem Nullartikel in adverbialer Verwendung – z.B. nach Belieben, auf Knopfdruck, ohne Ende oder bei Nacht – sind ein in der Mehrwortforschung bisher eher vernachlässigter Typ. Sie sind Untersuchungsgegenstand des laufenden Forschungsprojekts „Präpositionale Wortverbindungen kontrastiv“ (beteiligte Institutionen: IDS Mannheim, Universität Santiago de Compostela, Universität Trnava), in das wir in unserem Vortrag einen Einblick vermitteln. Es wird skizziert, wie sich solche Wortverbindungen sowie abstraktere präpositionale Wortverbindungsmuster vom Typ [in + SUBX-Zeit(en) (z.B. in Echtzeit, in Krisenzeiten) aus kontrastiver Sicht (Deutsch – Spanisch – Slowakisch) korpusbasiert untersuchen und lexikografisch beschreiben lassen. Von großem Interesse – gerade auch für Fremdsprachenlerner – sind dabei insbesondere die semantisch-funktionalen Restriktionen, denen solche Entitäten unterliegen. Basierend auf den theoretischen und empirischen Grundannahmen des am IDS entwickelten Modells „Usuelle Wortverbindungen“ (vgl. Steyer 2013) werden im Projekt zunächst Kollokations- und Kotextmuster für die binären deutschen Mehrworteinheiten induktiv in sehr großen Korpora ermittelt; im Anschluss werden sie einem systematischen Vergleich mit dem Spanischen und Slowakischen unterzogen. Methodisch greifen wir – in allen drei Sprachen – u.a. auf Kookkurrenzprofile zu den Wortverbindungen sowie auf Slotanalysen zu definierten Suchmustern zurück. Ziel des Projekts ist u.a. die Entwicklung eines neuartigen Prototyps für eine multilinguale Aufbereitung des Untersuchungsgegentands (speziell für Fremdsprachenlerner).
In this paper, we describe preliminary results from an ongoing experiment wherein we classify two large unstructured text corpora—a web corpus and a newspaper corpus—by topic domain (or subject area). Our primary goal is to develop a method that allows for the reliable annotation of large crawled web corpora with meta data required by many corpus linguists. We are especially interested in designing an annotation scheme whose categories are both intuitively interpretable by linguists and firmly rooted in the distribution of lexical material in the documents. Since we use data from a web corpus and a more traditional corpus, we also contribute to the important field of corpus comparison and corpus evaluation. Technically, we use (unsupervised) topic modeling to automatically induce topic distributions over gold standard corpora that were manually annotated for 13 coarse-grained topic domains. In a second step, we apply supervised machine learning to learn the manually annotated topic domains using the previously induced topics as features. We achieve around 70% accuracy in 10-fold cross validations. An analysis of the errors clearly indicates, however, that a revised classification scheme and larger gold standard corpora will likely lead to a substantial increase in accuracy.