  We consider a joint information extraction (IE) model, solving named entity
recognition, coreference resolution and relation extraction jointly over the
whole document. In particular, we study how to inject information from a
knowledge base (KB) in such IE model, based on unsupervised entity linking. The
used KB entity representations are learned from either (i) hyperlinked text
documents (Wikipedia), or (ii) a knowledge graph (Wikidata), and appear
complementary in raising IE performance. Representations of corresponding
entity linking (EL) candidates are added to text span representations of the
input document, and we experiment with (i) taking a weighted average of the EL
candidate representations based on their prior (in Wikipedia), and (ii) using
an attention scheme over the EL candidate list. Results demonstrate an increase
of up to 5% F1-score for the evaluated IE tasks on two datasets. Despite a
strong performance of the prior-based model, our quantitative and qualitative
analysis reveals the advantage of using the attention-based approach.
