  Past work in relation extraction mostly focuses on binary relation between
entity pairs within single sentence. Recently, the NLP community has gained
interest in relation extraction in entity pairs spanning multiple sentences. In
this paper, we propose a novel architecture for this task: inter-sentential
dependency-based neural networks (iDepNN). iDepNN models the shortest and
augmented dependency paths via recurrent and recursive neural networks to
extract relationships within (intra-) and across (inter-) sentence boundaries.
Compared to SVM and neural network baselines, iDepNN is more robust to false
positives in relationships spanning sentences.
  We evaluate our models on four datasets from newswire (MUC6) and medical
(BioNLP shared task) domains that achieve state-of-the-art performance and show
a better balance in precision and recall for inter-sentential relationships. We
perform better than 11 teams participating in the BioNLP shared task 2016 and
achieve a gain of 5.2% (0.587 vs 0.558) in F1 over the winning team. We also
release the crosssentence annotations for MUC6.
