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-====== Partners ======+====== KEEPHA Partners ======
 ===== DFKI ===== ===== DFKI =====
 The German Research Center for Artificial Intelligence (DFKI) is Germany's leading business-oriented research institution in the field of innovative software technologies based on artificial intelligence methods. The Speech and Language Technology Lab (SLT-Lab) is a worldwide recognized research group for natural language processing with various focal points in all important areas of language technology, such as information extraction, big text data analysis and semantic search as well as biomedical AI research. Prof. Dr.-Ing. Sebastian Möller is the head of the Speech and Language Technology Lab and professor at the Technical University of Berlin. His research group at DFKI has a strong background in corpus generation (Cotik et al. 2017, Schiersch et al., 2018), general information extraction (Alt et al., 2019a, Alt et al. 2019b), as well as processing biomedical text (Kirschnick et al., 2018, Roller et al., 2018). It researches neural information- and relation-extraction with regard to explanatory methods and continuous learning in neural networks as well as scalable learning for medical prognosis models. The SLT-Lab is a member of the Berlin Big Data Center (BBDC) and is involved in the projects DeepLee, MACSS, and BigMedilytics. The German Research Center for Artificial Intelligence (DFKI) is Germany's leading business-oriented research institution in the field of innovative software technologies based on artificial intelligence methods. The Speech and Language Technology Lab (SLT-Lab) is a worldwide recognized research group for natural language processing with various focal points in all important areas of language technology, such as information extraction, big text data analysis and semantic search as well as biomedical AI research. Prof. Dr.-Ing. Sebastian Möller is the head of the Speech and Language Technology Lab and professor at the Technical University of Berlin. His research group at DFKI has a strong background in corpus generation (Cotik et al. 2017, Schiersch et al., 2018), general information extraction (Alt et al., 2019a, Alt et al. 2019b), as well as processing biomedical text (Kirschnick et al., 2018, Roller et al., 2018). It researches neural information- and relation-extraction with regard to explanatory methods and continuous learning in neural networks as well as scalable learning for medical prognosis models. The SLT-Lab is a member of the Berlin Big Data Center (BBDC) and is involved in the projects DeepLee, MACSS, and BigMedilytics.
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