  Understanding the meaning of text often involves reasoning about entities and
their relationships. This requires identifying textual mentions of entities,
linking them to a canonical concept, and discerning their relationships. These
tasks are nearly always viewed as separate components within a pipeline, each
requiring a distinct model and training data. While relation extraction can
often be trained with readily available weak or distant supervision, entity
linkers typically require expensive mention-level supervision -- which is not
available in many domains. Instead, we propose a model which is trained to
simultaneously produce entity linking and relation decisions while requiring no
mention-level annotations. This approach avoids cascading errors that arise
from pipelined methods and more accurately predicts entity relationships from
text. We show that our model outperforms a state-of-the art entity linking and
relation extraction pipeline on two biomedical datasets and can drastically
improve the overall recall of the system.
