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We consider a joint information extraction (IE) model, solving named entityrecognition, coreference resolution and relation extraction jointly over thewhole document. In particular, we study how to inject information from aknowledge base (KB) in such IE model, based on unsupervised entity linking. Theused KB entity representations are learned from either (i) hyperlinked textdocuments (Wikipedia), or (ii) a knowledge graph (Wikidata), and appearcomplementary in raising IE performance. Representations of correspondingentity linking (EL) candidates are added to text span representations of theinput document, and we experiment with (i) taking a weighted average of the ELcandidate representations based on their prior (in Wikipedia), and (ii) usingan attention scheme over the EL candidate list. Results demonstrate an increaseof up to 5% F1-score for the evaluated IE tasks on two datasets. Despite astrong performance of the prior-based model, our quantitative and qualitativeanalysis reveals the advantage of using the attention-based approach.

Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution