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The CSS group currently holds courses and supervises bachelor's and master's theses covering contents from the the following research areas. Details can be found on the course page and the thesis page.

Computational Argumentation

Computational argumentation deals with the computational analysis and synthesis of natural language arguments and argumentation, usually in an empirical data-driven manner. The covered tasks range from the mining of arguments from text, over the assessment of argumentation properties, to the generation of new arguments and argumentative texts.

Computational argumentation is currently most studied in the context of natural language processing, but also covers aspects of information retrieval, machine learning, classical artificial intelligence, logic and reasing, as well as visualization and human-computer interaction.

Text Mining

Text mining deals with the automatic or semi-automatic discovery of new, previously unknown information of high quality from large numbers of unstructured texts. The types of information to be inferred from the texts are usually specified beforehand, i.e., text mining tackles given tasks.

Text mining commonly requires to perform three steps in sequence, each of which can be associated to one field: Information retrieval, to gather input texts that are potentially relevant for the task, natural language processing, to analyze input texts in order to identify and structure relevant information, and data mining, to discover patterns in the structured information that has been inferred from the texts.

Teaching pages

Die Universität der Informationsgesellschaft