  We study the problem of extracting N-ary relation tuples from scientific
articles. This task is challenging because the target knowledge tuples can
reside in multiple parts and modalities of the document. Our proposed method
ReSel decomposes this task into a two-stage procedure that first retrieves the
most relevant paragraph/table and then selects the target entity from the
retrieved component. For the high-level retrieval stage, ReSel designs a simple
and effective feature set, which captures multi-level lexical and semantic
similarities between the query and components. For the low-level selection
stage, ReSel designs a cross-modal entity correlation graph along with a
multi-view architecture, which models both semantic and document-structural
relations between entities. Our experiments on three scientific information
extraction datasets show that ReSel outperforms state-of-the-art baselines
significantly.
