ArgumenText is a validation project of the Ubiquitous Knowledge Processing (UKP) Lab at the Technische Universität Darmstadt. It is our goal to validate the latest research breakthroughs in Argument Mining and Text Analytics in industrial applications and to develop innovative products to unleash the potential of unstructured data. Starting with a previously developed joint-modeling method for identifying argument structures in student essays (Stab and Gurevych 2017), we develop robust end-to-end approaches for mining arguments from web-scale corpora.
We extent our current methods to other languages like German using Language Adaptation and evaluate their robustness across various topics and text types. The resulting methods and software are intended to extract arguments from dynamic text sources such as news streams or social media in real-time and to prepare them for the user in a comprehensive summary.
ArgumenText is funded by the Federal Ministry of Education and Research (BMBF) as part of the VIP+ programme.
We develop novel deep learning methods for robustly mining arguments from heterogeneous Web sources and text streams like social media and news. Our approach allows for fast adaptation to new use cases and mining arguments robustly across different domains.
Our argument mining methods will be optimized for finding arguments in different languages. We build on the latest multilingual representations allowing us to make optimal use of our training resources and to apply a model trained in one language to many other language.
For allowing a fast and comprehensive overview of topic-relevant arguments, we develop summarization methods that group similar pro and con arguments from different sources. This allows easy access to relevant arguments without reading through long result lists.
To find trending arguments, we apply our methods to text streams like news or social media. We seek to develop a scalable infrastructure that continuously searches millions of different sources for the most recent arguments and delivers the latest trends in real-time.