  Information Extraction (IE) from scientific texts can be used to guide
readers to the central information in scientific documents. But narrow IE
systems extract only a fraction of the information captured, and Open IE
systems do not perform well on the long and complex sentences encountered in
scientific texts. In this work we combine the output of both types of systems
to achieve Semi-Open Relation Extraction, a new task that we explore in the
Biology domain. First, we present the Focused Open Biological Information
Extraction (FOBIE) dataset and use FOBIE to train a state-of-the-art narrow
scientific IE system to extract trade-off relations and arguments that are
central to biology texts. We then run both the narrow IE system and a
state-of-the-art Open IE system on a corpus of 10k open-access scientific
biological texts. We show that a significant amount (65%) of erroneous and
uninformative Open IE extractions can be filtered using narrow IE extractions.
Furthermore, we show that the retained extractions are significantly more often
informative to a reader.
