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Neues von KorAP
(2019)
Der Beitrag stellt dar, in welch hohem Maße sprachliche Spielfreude zur Ausbildung großer Kompositagruppen rund um ein anregendes Musterwort führen kann und wie dieser Spieltrieb auch eine Fülle von Varianten zu interessanten literarischen oder politischen Formulierungen hervorbringt. Um solche Formulierspiele quer durch aktuelle Zeitungen in Deutschland, in Österreich und in der Schweiz verfolgen zu können, empfiehlt sich die Benutzung eines großen digitalen Erfassungssytems wie COSMAS II aus dem Institut für Deutsche Sprache in Mannheim.
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.
We describe a systematic and application-oriented approach to training and evaluating named entity recognition and classification (NERC) systems, the purpose of which is to identify an optimal system and to train an optimal model for named entity tagging DeReKo, a very large general-purpose corpus of contemporary German (Kupietz et al., 2010). DeReKo 's strong dispersion wrt. genre, register and time forces us to base our decision for a specific NERC system on an evaluation performed on a representative sample of DeReKo instead of performance figures that have been reported for the individual NERC systems when evaluated on more uniform and less diverse data. We create and manually annotate such a representative sample as evaluation data for three different NERC systems, for each of which various models are learnt on multiple training data. The proposed sampling method can be viewed as a generally applicable method for sampling evaluation data from an unbalanced target corpus for any sort of natural language processing.
^This paper describes DeReKo (Deutsches Referenzkorpus), the Archive of General Reference Corpora of Contemporary Written German at the Institut für Deutsche Sprache (IDS) in Mannheim, and the rationale behind its development. We discuss its design, its legal background, how to access it, available metadata, linguistic annotation layers, underlying standards, ongoing developments, and aspects of using the archive for empirical linguistic research. The focus of the paper is on the advantages of DEREKO’s design as a primordial sample from which virtual corpora can be drawn for the specific purposes of individual studies. Both concepts, primordial sample and virtual corpus are explained and illustrated in detail. Furthermore, we describe in more detail how DEREKO deals with the fact that all its texts are subject to third parties’ intellectual property rights, and how it deals with the issue of replicability, which is particularly challenging given DEREKO’s dynamic growth and the possibility to construct from it an open number of virtual corpora.