  Relation Extraction (RE) aims to label relations between groups of marked
entities in raw text. Most current RE models learn context-aware
representations of the target entities that are then used to establish relation
between them. This works well for intra-sentence RE and we call them
first-order relations. However, this methodology can sometimes fail to capture
complex and long dependencies. To address this, we hypothesize that at times
two target entities can be explicitly connected via a context token. We refer
to such indirect relations as second-order relations and describe an efficient
implementation for computing them. These second-order relation scores are then
combined with first-order relation scores. Our empirical results show that the
proposed method leads to state-of-the-art performance over two biomedical
datasets.
