In this paper, we propose a new framework that unifies the output of three information extraction (IE) tasks -entity mentions, relations and events as an information network representation, and extracts all of them using one single joint model based on structured prediction. This novel formulation allows different parts of the information network fully interact with each other. For example, many relations can now be considered as the resultant states of events. Our approach achieves substantial improvements over traditional pipelined approaches, and significantly advances state-of-the-art end-toend event argument extraction.
To use more information from the sentence, REF proposed to extract entity mentions, relations and events in ACE task based on the unified structure.
Constructing Information Networks Using One Single Model
EMNLP
Computer Science