  Fact triples are a common form of structured knowledge used within the
biomedical domain. As the amount of unstructured scientific texts continues to
grow, manual annotation of these texts for the task of relation extraction
becomes increasingly expensive. Distant supervision offers a viable approach to
combat this by quickly producing large amounts of labeled, but considerably
noisy, data. We aim to reduce such noise by extending an entity-enriched
relation classification BERT model to the problem of multiple instance
learning, and defining a simple data encoding scheme that significantly reduces
noise, reaching state-of-the-art performance for distantly-supervised
biomedical relation extraction. Our approach further encodes knowledge about
the direction of relation triples, allowing for increased focus on relation
learning by reducing noise and alleviating the need for joint learning with
knowledge graph completion.
