  Past work in relation extraction has focused on binary relations in single
sentences. Recent NLP inroads in high-value domains have sparked interest in
the more general setting of extracting n-ary relations that span multiple
sentences. In this paper, we explore a general relation extraction framework
based on graph long short-term memory networks (graph LSTMs) that can be easily
extended to cross-sentence n-ary relation extraction. The graph formulation
provides a unified way of exploring different LSTM approaches and incorporating
various intra-sentential and inter-sentential dependencies, such as sequential,
syntactic, and discourse relations. A robust contextual representation is
learned for the entities, which serves as input to the relation classifier.
This simplifies handling of relations with arbitrary arity, and enables
multi-task learning with related relations. We evaluate this framework in two
important precision medicine settings, demonstrating its effectiveness with
both conventional supervised learning and distant supervision. Cross-sentence
extraction produced larger knowledge bases. and multi-task learning
significantly improved extraction accuracy. A thorough analysis of various LSTM
approaches yielded useful insight the impact of linguistic analysis on
extraction accuracy.
