In this collaborative project, we are using Natural Language Processing and Machine Learning to identify secondary practical uses of research findings from final reports of grant funded work. Such reports are often stored in specialized databases, where long-term archiving activities focus on standardization, interoperability, and information indexing and retrieval. However, secondary use of reports is often not enabled or enforced, limiting the replication and reusability of research. We are identifying practically relevant patterns from text data by using information extraction techniques and detect transferable knowledge (from basic research to applications) in selected domains.
- Witt, A., Bopp, J., Fiedler, N., Steffen, D., Diesner, J., Rezapour, R., Raster, M., Wockenfuß, J., Köller, C. (2018). Impact of research for the non-academic community: A new classification scheme. Proceedings of the 1st Workshop on Computational Impact Detection from Text Data, 11th Language Resources and Evaluation Conference (LREC 2018), Miyazaki, Japan.
- Federal Ministry of Education and Research (Germany) and Institute for German Language (IDS) (Germany), 2017-2019