  Most modern Information Extraction (IE) systems are implemented as sequential
taggers and only model local dependencies. Non-local and non-sequential context
is, however, a valuable source of information to improve predictions. In this
paper, we introduce GraphIE, a framework that operates over a graph
representing a broad set of dependencies between textual units (i.e. words or
sentences). The algorithm propagates information between connected nodes
through graph convolutions, generating a richer representation that can be
exploited to improve word-level predictions. Evaluation on three different
tasks --- namely textual, social media and visual information extraction ---
shows that GraphIE consistently outperforms the state-of-the-art sequence
tagging model by a significant margin.
