Introducing RusDraCor – A TEI-Encoded Russian Drama Corpus for the Digital Literary Studies
We describe the creation of a corpus of Russian-language drama, comprising hundreds of texts from the middle of the 18th century to the first third of the 20th century. Texts are encoded in the XML-based markup standard TEI, the focus is on extra-linguistic, structural annotations, although additional annotation layers can be added easily.
Conference abstracts for DHd2017, Bern. (http://www.dhd2017.ch/)
Das Projekt ‘Digitale Netzwerkanalyse dramatischer Texte’ steht in der Tradition strukturanalytischer Ans¨atze in der Literaturwissenschaft (allgemein Titzmann 1977), die es einerseits im Sinne eines konsequent netzwerkanalytischen Relationismus (mit Rekurs auf die Social Network Analysis, siehe u. a. Wasserman/Faust 1998), andererseits unterstutzt durch Verfahren der automatisierten ¨ Datenerhebung und -auswertung weiterentwickelt, um sie auf gr¨oßere Textkorpora anzuwenden und so umfassende relationale Daten uber Prozesse des literaturgeschichtlichen Strukturwandels ¨ gewinnen zu k¨onnen.
Of late, the network analysis of literary texts has grown into an independent research field of digital literary studies. Since analysing the network structure of unique texts promises just marginal results, the perspective should shift towards a ‘distant reading’ of hundreds or thousands of texts. In this paper, we describe how the process of formalising literary data is facilitated by machine-readable corpora comprising hundreds of dramatic texts in several languages. Taking a corpus of roughly 500 German-language dramas as an example, we demonstrate how the calculation of network metrics and visualisations can deliver new material for interpretation and offer new insights into the evolution of drama.
The problem of link prediction gathered a lot of attention in the last few years, arising in dierent applications ranging from recommendation systems to social networks. In this paper, we will describe the most popular similarity indices, compare their performance in their ability to show links with the highest probability of being removed from initial network and describe the approach that allows to use them to predict missing links using supervised machine learning. We will show the accuracy of prediction of this method on examples of real networks.
Contributions in this volume focus on computationally efficient algorithms and rigorous mathematical theories for analyzing large-scale networks. Researchers and students in mathematics, economics, statistics, computer science and engineering will find this collection a valuable resource filled with the latest research in network analysis. Computational aspects and applications of large-scale networks in market models, neural networks, social networks, power transmission grids, maximum clique problem, telecommunication networks, and complexity graphs are included with new tools for efficient network analysis of large-scale networks.
This proceeding is a result of the 7th International Conference in Network Analysis, held at the Higher School of Economics, Nizhny Novgorod in June 2017. The conference brought together scientists, engineers, and researchers from academia, industry, and government.
Our paper offers a critical examination of the concept and practice of Distant Reading, as coined by Franco Moretti in 2000. We consider several definitions of the term and look for possible operationalizations. It becomes clear that Distant Reading has largely been a theoretical vehicle or mere buzzword in the past one and a half decades that adapts only slowly to the practices and technological standards of the Digital Humanities. In the light of these findings, we conclude with an examination of the operational potential of Foucauldian discourse analysis.
The article depicts the problem of interpretation of Schiller's work in Tieck's literary and theatre criticism.