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Das Beispiel ist seit der Antike ein zentraler Gegenstand der abendländischen Diskussion. In dieser ersten umfassenden Monographie zur Linguistik des Beispiels wird deshalb eine interdisziplinäre Perspektive entfaltet, in der Ansätze aus Rhetorik, Philosophie, Pädagogik und Psychologie sowie linguistischen Ansätze zur Beispielforschung behandelt werden. Die sprachwissenschaftliche Beschäftigung mit Beispielen blieb bisher jedoch ein Randphänomen, obwohl Praktiken der Beispielverwendung in der Alltagskommunikation allgegenwärtig sind.
Orientiert an ›grounded theory‹, linguistischer Hermeneutik und Handlungssemantik wird hier ein Beispielbegriff erarbeitet, demzufolge das Beispielverwenden eine komplexe Form sprachlichen Handelns und eine fundamentale menschliche Denkbewegung darstellt, die darin besteht, einen Konnex zwischen Besonderem und Allgemeinem zu konstituieren. Hierauf basierend werden Beispiele anhand eines umfangreichen Korpus von Gesprächsdaten analysiert und kommunikative Muster, sprachliche Realisierungsformen sowie Funktionen des Beispielverwendens in der Interaktion herausgearbeitet.
Vernetzung statt Vereinheitlichung. Digitale Forschungsinfrastrukturen in den Geisteswissenschaften
(2014)
Die Entwicklung der digitalen Infrastruktur am Hamburger Zentrum für Sprachkorpora (HZSK) kann als Beispiel für die Evolution individueller technischer Einzellösungen hin zu fachspezifischen virtuellen Arbeits- und Forschungsumgebungen, die im Rahmen supranationaler Forschungsinfrastrukturen für die digitalen Geisteswissenschaften miteinander vernetzt sind, angesehen werden. Im Fokus steht im konkreten Fall des HZSK die Sicherung der langfristigen Zugänglichkeit von Forschungsdaten (multimedialen Daten gesprochener Sprache) durch die Entwicklung einer virtuellen Forschungsumgebung, die einerseits an die zentrenbasierte Forschungsinfrastruktur CLARIN-D angebunden ist und andererseits fachspezifische Benutzerschnittstellen schafft.
The variation of the strong genitive marker of the singular noun has been treated by diverse accounts. Still there is a consensus that it is to a large extent systematic but can be approached appropriately only if many heterogeneous factors are taken into account. Over thirty variables influencing this variation have been proposed. However, it is actually unclear how effective they can be, and above all, how they interact. In this paper, the potential influencing variables are evaluated statistically in a machine learning approach and modelled in decision trees in order to predict the genitive marking variants. Working with decision trees based exclusively on statistically significant data enables us to determine what combination of factors is decisive in the choice of a marking variant of a given noun. Consequently the variation factors can be assessed with respect to their explanatory power for corpus data and put in a hierarchized order.
We present a novel NLP resource for the explanation of linguistic phenomena, built and evaluated exploring very large annotated language corpora. For the compilation, we use the German Reference Corpus (DeReKo) with more than 5 billion word forms, which is the largest linguistic resource worldwide for the study of contemporary written German. The result is a comprehensive database of German genitive formations, enriched with a broad range of intra- und extralinguistic metadata. It can be used for the notoriously controversial classification and prediction of genitive endings (short endings, long endings, zero-marker). We also evaluate the main factors influencing the use of specific endings. To get a general idea about a factor’s influences and its side effects, we calculate chi-square-tests and visualize the residuals with an association plot. The results are evaluated against a gold standard by implementing tree-based machine learning algorithms. For the statistical analysis, we applied the supervised LMT Logistic Model Trees algorithm, using the WEKA software. We intend to use this gold standard to evaluate GenitivDB, as well as to explore methodologies for a predictive genitive model.
Part-of-speech tagging (POS-tagging) of spoken data requires different means of annotation than POS-tagging of written and edited texts. In order to capture the features of German spoken language, a distinct tagset is needed to respond to the kinds of elements which only occur in speech. In order to create such a coherent tagset the most prominent phenomena of spoken language need to be analyzed, especially with respect to how they differ from written language. First evaluations have shown that the most prominent cause (over 50%) of errors in the existing automatized POS-tagging of transcripts of spoken German with the Stuttgart Tübingen Tagset (STTS) and the treetagger was the inaccurate interpretation of speech particles. One reason for this is that this class of words is virtually absent from the current STTS. This paper proposes a recategorization of the STTS in the field of speech particles based on distributional factors rather than semantics. The ultimate aim is to create a comprehensive reference corpus of spoken German data for the global research community. It is imperative that all phenomena are reliably recorded in future part-of-speech tag labels.
Machine learning methods offer a great potential to automatically investigate large amounts of data in the humanities. Our contribution to the workshop reports about ongoing work in the BMBF project KobRA (http://www.kobra.tu-dortmund.de) where we apply machine learning methods to the analysis of big corpora in language-focused research of computer-mediated communication (CMC). At the workshop, we will discuss first results from training a Support Vector Machine (SVM) for the classification of selected linguistic features in talk pages of the German Wikipedia corpus in DeReKo provided by the IDS Mannheim. We will investigate different representations of the data to integrate complex syntactic and semantic information for the SVM. The results shall foster both corpus-based research of CMC and the annotation of linguistic features in CMC corpora.
Maximizing the potential of very large corpora: 50 years of big language data at IDS Mannheim
(2014)
Very large corpora have been built and used at the IDS since its foundation in 1964. They have been made available on the Internet since the beginning of the 90’s to currently over 30,000 researchers worldwide. The Institute provides the largest archive of written German (Deutsches Referenzkorpus, DeReKe) which has recently been extended to 24 billion words. DeReKe has been managed and analysed by engines known as COSMAS and afterwards COSMAS II, which is currently being replaced by a new, scalable analysis platform called KorAP. KorAP makes it possible to manage and analyse texts that are accompanied by multiple, potentially conflicting, grammatical and structural annotation layers, and is able to handle resources that are distributed across different, and possibly geographically distant, storage systems. The majority of texts in DeReKe are not licensed for free redistribution, hence, the COSMAS and KorAP systems offer technical solutions to facilitate research on very large corpora that are not available (and not suitable) for download. For the new KorAP system, it is also planned to provide sandboxed environments to support non-remote-API access “near the data” through which users can run their own analysis programs.
As a result of legal restrictions the Google Ngram Corpora datasets are a) not accompanied by any metadata regarding the texts the corpora consist of and the data are b) truncated to prevent an indirect conclusion from the n-gram to the author of the text. Some of the consequences of this strategy are discussed in this article.