Experimental software from the research projects of UKP can be found at GitHub: https://github.com/ukplab
German Research Foundation (DFG)
Learning new languages became very popular in recent years. Although many language learning platforms offer free-of-charge lessons for many languages, their exercises are often not challenging enough, as they are either too easy or too difficult for effective learning. We tackle this issue by adjusting the difficulty of C-test exercises to meet the demands of the learners. C-tests are a special kind of cloze test, in which learners have to complete the second half of every second word. They are frequently used as language proficiency tests, as they allow a learner to train morphological, syntactic, and semantic properties of a language at the same time.
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.
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.
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.
The project aims at using natural language processing techniques to analyze educational information and answer user questions on various educational topics. Since a large portion of users' questions have already been asked by other people in community question answering forums and answered by educational experts or crowds, we use the available question and answer archives to answer these questions and minimize human effort in searching through educational information. The project consists of different components including question classification, question and answer retrieval, answer quality assessment, and summarization.
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.
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.
Federal Ministry of Education and Research (BMBF)
FOCUS is a joint research project within the framework of the BMBF funding program “Digital Change in Education, Science and Research” and is set up in accordance with the funding guidelines for the research of management of research data and its life cycle at universities and other research institutions.
The objective of the project is to develop subject-specific modular training courses in the area of research data management and archiving, to establish them permanently at the respective universities and thus to make offers to the Hessian universities for the subsequent use of the relevant training modules.
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.
European Commission (EU)
OpenMinTeD aspires to enable the creation of an infrastructure that fosters and facilitates the discovery and use of text mining technologies and interoperable services. It examines several use cases identified by experts from different scientific areas, ranging from generic scholarly communication to literature related to life sciences, food and agriculture, and social sciences and humanities.
Long-term UKP team projects
The DKPro Repository consists of a growing number of scalable, robust and flexible UIMA components for various kinds of NLP tasks such as tokenization, sentence splitting, PoS tagging, negation detection, lexical chaining, word pair extraction.
Feel free to download our DKPro Flyer.
UBY – Large-scale Sense-linked Lexical-semantic Resource
UBY is a large-scale lexical-semantic resource for natural language processing (NLP) based on the ISO standard Lexical Markup Framework (LMF). Most UBY related software is developed open source on Google Code. UBY combines a wide range of information from expert-constructed and collaboratively constructed resources for English and German.
The main topic of this project is on the application of machine learning techniques in audiovisual content from the digital humanities. This research employs well established methods from areas such as natural language processing, speech signal processing and computer vision in audiovisual recordings used in research from the humanities, such as psychology, communication sciences and pedagogy.