Prof. Dr. Sebastian Haunss

Some sort of medical infrastructure has existed in all modern states and dependent territories. However, healthcare systems that provide legal entitlements to medical care at least for specific groups of the population, and that regulate access to and provision of healthcare on a national level only came into existence at the end of the nineteenth century.

This article investigates the integration of machine learning in the political claim annotation workflow with the goal to partially automate the annotation and analysis of large text corpora. It introduces the MARDY annotation environment and presents results from an experiment in which the annotation quality of annotators with and without machine learning based annotation support is compared.

Political actors cooperate with each other to share resources and to organize political support. In this article, we describe and explain such cooperative behavior in European Union policy-making by analyzing the information networks that parliamentarians of the Bundestag entertain with other party politicians and with interest groups. First, we describe whom parliamentarians cooperate with to receive policy information. Subsequently, we identify different types of cooperation networks.

This paper describes the MARDY corpus annotation environment developed for a collaboration between political science and computational linguistics. The tool realizes the complete workflow necessary for annotating a large newspaper text collection with rich information about claims (demands) raised by politicians and other actors, including claim and actor spans, relations, and polarities. In addition to the annotation GUI, the tool supports the identification of relevant documents, text pre-processing, user management, integration of external knowledge bases, annotation comparison and merging, statistical analysis, and the incorporation of machine learning models as “pseudo-annotators”.