We investigate the task of joint entity relation extraction. Unlike prior efforts, we propose a new lightweight joint learning paradigm based on minimum risk training (MRT). Specifically, our algorithm optimizes a global loss function which is flexible and effective to explore interactions between the entity model and the relation model. We implement a strong and simple neural network where the MRT is executed. Experiment results on the benchmark ACE05 and NYT datasets show that our model is able to achieve state-of-the-art joint extraction performances.
REF optimize a global loss function to jointly train the two models under the framework work of Minimum Risk Training.
Extracting Entities and Relations with Joint Minimum Risk Training
EMNLP
Computer Science