  Relation extraction is the task of determining the relation between two
entities in a sentence. Distantly-supervised models are popular for this task.
However, sentences can be long and two entities can be located far from each
other in a sentence. The pieces of evidence supporting the presence of a
relation between two entities may not be very direct, since the entities may be
connected via some indirect links such as a third entity or via co-reference.
Relation extraction in such scenarios becomes more challenging as we need to
capture the long-distance interactions among the entities and other words in
the sentence. Also, the words in a sentence do not contribute equally in
identifying the relation between the two entities. To address this issue, we
propose a novel and effective attention model which incorporates syntactic
information of the sentence and a multi-factor attention mechanism. Experiments
on the New York Times corpus show that our proposed model outperforms prior
state-of-the-art models.
