End-to-end neural relation extraction using deep biaffine attention
DQ Nguyen, K Verspoor
Lecture Notes in Computer Science | SpringerLink | Published : 2019
© Springer Nature Switzerland AG 2019. We propose a neural network model for joint extraction of named entities and relations between them, without any hand-crafted features. The key contribution of our model is to extend a BiLSTM-CRF-based entity recognition model with a deep biaffine attention layer to model second-order interactions between latent features for relation classification, specifically attending to the role of an entity in a directional relationship. On the benchmark “relation and entity recognition” dataset CoNLL04, experimental results show that our model outperforms previous models, producing new state-of-the-art performances.
Related Projects (3)
Awarded by Australian Research Council
This work was supported by the ARC projects DP150101550 and LP160101469.