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Most work in relation extraction forms a prediction by looking at a shortspan of text within a single sentence containing a single entity pair mention.This approach often does not consider interactions across mentions, requiresredundant computation for each mention pair, and ignores relationshipsexpressed across sentence boundaries. These problems are exacerbated by thedocument- (rather than sentence-) level annotation common in biological text.In response, we propose a model which simultaneously predicts relationshipsbetween all mention pairs in a document. We form pairwise predictions overentire paper abstracts using an efficient self-attention encoder. All-pairsmention scores allow us to perform multi-instance learning by aggregating overmentions to form entity pair representations. We further adapt to settingswithout mention-level annotation by jointly training to predict named entitiesand adding a corpus of weakly labeled data. In experiments on two Biocreativebenchmark datasets, we achieve state of the art performance on the BiocreativeV Chemical Disease Relation dataset for models without external KB resources.We also introduce a new dataset an order of magnitude larger than existinghuman-annotated biological information extraction datasets and more accuratethan distantly supervised alternatives.

Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction