Retrieving passages instead of whole documents can help professionals to acquire new information faster. This is important in domains where time for research is limited and expensive. For example a medical doctor at
an hospital has usually less than an hour per day to look up fresh information for rare cases. We present an approach for retrieving relevant passages in a document collection which leverages two orthogonal semantic embeddings. For this occasion we demonstrate a first prototype implementation as described in . In this talk, we give an overview of our approach to learn a joint vector space representation of these embeddings. We plan to exploit this model to further improve passage and document retrieval tasks.
 R. Schneider, S. Arnold, T. Oberhauser, T. Klatt, T. Steffek, and A. Löser, “Smart-MD: Neural Paragraph Retrieval of Medical Topics,” in Companion of the The Web Conference 2018 on The Web Conference 2018, WWW 2018, Lyon , France, April 23-27, 2018, 2018, pp. 203–206.