Experimental software from the research projects of UKP can be found at GitHub: https://github.com/ukplab
German Research Foundation (DFG)
Argumentation mining deals with the automatic identification of arguments and their relations from natural language text. This research project targets at the specific challenges of argumentation mining for the web. We seek to establish foundations of algorithms that apply argument mining to various forms of web argumentation, efficiently leverage the scale of the web, and complement argumentation mining with an argumentation analysis to effectively assess important quality dimensions.
Dictionaries are an essential resource in many domains of research, education, and natural language processing (NLP). One crucial part of dictionaries are example sentences which illustrate real-world use cases of a lemma. However, finding good example sentences in large corpora imposes a heavy workload on lexicographers. In this project, we develop a novel system which eases the work of lexicographers by interactively assessing the goodness and diversity of dictionary examples.
The annotation of specific semantic phenomena often require compiling task-specific corpora and creating or extending task-specific knowledge bases. Presently, researchers require a broad range of skills and tools to address such semantic annotation tasks. INCEpTION aims towards building an annotation platform that incorporates corpus extraction, annotation, and knowledge management into a joint platform.
Funded by AIPHES, this workshop for pupils in 6th/7th grade promotes women in STEM. Lead by female computer science students from TU Darmstadt, the pupils gain some data analytics and programming skills.
The DIP project – an international cooperation with Bar-Ilan University and Israel Institute of Technology – aims at the next big step in information access technology. The goal is to support users in identifying and assimilating the large set of relevant statements found within multitudes of documents which are usually retrieved by the current search technologies. Novel methods for statement extraction, information consolidation, and inferring relations represent the core research areas within this project.
AIPHES develops new methods to deal with information overload by summarizing multiple documents to a condensed summary. We develop adaptive methods to create summaries of any type from multiple sources and across different genres. To do so, we combine different methodological backgrounds – computational linguistics, computer science, machine learning – to approach the task of extracting, summarizing and evaluating textual information from different sources.
This project aims at investigating computational methods that continuously improve their capability to recognize arguments in ongoing debates, align incomplete arguments with previous arguments and enrich them with automatically acquired background knowledge, and constantly extend semantic knowledge bases with information required to understand arguments.
Federal Ministry of Education and Research (BMBF)
FAMULUS (Fostering diagnostic competence in medical and teacher education via adaptive online-case-simulations)
The interdisciplinary FAMULUS project aims to study how online case simulations that provide automatic adaptive feedback can foster students' diagnostic skills. To generate automatic feedback, we will develop novel methods for identifying and evaluating diagnostic reasoning (e.g. hypothesis generation, evidence generation and evaluation, hypothesis acceptance or rejection) in student essays. The effect of such feedback on the development of diagnostic skills will then be evaluated in a user study with students from medicine and education.
In order to make informed decisions, appropriate arguments are needed. However, the mere amount of information and the complexity of many questions frequently prevents us from finding all arguments that are relevant for a reasonable decision. Within the “Decision support by means of automatically extracting natural language arguments from big data” (ArgumenText) project, the UKP Lab develops novel Argument Mining methods for extracting arguments from large and heterogeneous text sources in order to facilitate decision making processes. In response to a user-defined search query, neural networks determine relevant arguments in realtime and summarize them in a comprehensive way. In contrast to conventional systems, an argumentative information system can show the reasons for or against a decision.
CEDIFOR intends to contribute to bridging the gap between research in the Humanities and computer based methods, and help researchers to master the characteristic problems in this process. It is a Digital Humanities Centre providing methodological expertise for advising researchers from the Humanities, Social, and Educational Sciences on adopting computer based methods in their research. This concerns the planning and operational stage of projects as well as the long-term provision of result data.
Software Campus (BMBF)
Software Campus program is funded by Germany's Federal Ministry for Education and Research (BMBF).
In close collaboration with strong partners from industry and research, Software Campus participants develop innovative academic IT projects and benefit from an individually tailored training curriculum with outstanding academics and managers. The Federal Ministry of Education and Research (BMBF) provides funding of up to EUR 100,000 for each IT project.
UKP Lab is currently represented in the network by the following researchers:
- Tilman Beck: Identification and Aggregation of Arguments in Scientific Literature on Basis of Natural Language Processing
- Yevgeniy Puzikov: Text Generation for Tone of Voice and eCommerce
- Andreas Rücklé: Intelligent Search in a Social Web
The projects already finished within the scope of the program: