Organizations have access to huge amounts of text data but harnessing its business potential is still a major challenge. ArgumenText offers the solutions. We use Text Mining, Deep Learning and Big Data Analytics to unleash the potential of unstructured data and to integrate unused assets into decision-making processes.
Our technology allows searching large document collections for arguments. In response to a user-defined topic, neural networks determine relevant pro and con arguments in real-time, and represent them in a concise summary. Decision-relevant information can be found much faster and complex decision-making processes can be significantly improved by exploiting the potential of large text collections. Applied to text streams like news streams or social media, latest trends and innovations can be discovered with ease.
On October 11, Christian Stab presented our project to an international audience at the Language Technology Summit 2017 in Brussels. His talk, "Improving Decision Making with Argument Mining", presented our latest research results on how decision-making processes can be supported with Deep Learning. A summary of the talk can be found at:
On October 11, Johannes Daxenberger gave an invited 90-minutes talk at the Innovation Summit of Alexander Thamm GmbH in Munich (non-public event). He gave an introduction to text classification and deep learning for language technology, followed by an overview of the latest research results in argumentation mining and a project summary of ArgumenText, including demo session.
On September 10, Johannes Daxenberger and co-authors presented latest research finding about detecting claims (statements that should be supported with reasons) at the annual Conference on Empirical Methods in Natural Language Processing. Our goal in this work was to find and learn about claims in different textual domains including online discourse, legal text, and student essays. The extensive experiments carried out in this research showed that simple lexical clues are most helpful to detect claims across domains. The paper can be found here:
On September 10, our colleague Andreas Rücklé presented our latest work on detecting complex argumentative structures at the main event of the Natural Language Processing research community, the annual meeting of the Association for Computational Linguistics (ACL). We showed that an end-to-end approach (a deep learning system which models several tasks jointly) on detecting arguments in student essays has superior performance as compared to non-neural pipeline approaches. We also showed that modeling argument mining as a sequence tagging problem achieves state-of-the-art performance on this task. The paper can be found here: