End-to-end neural relation extraction using deep biaffine attention
This improves relation extraction for natural language processing tasks, but is incremental as it builds on existing neural methods.
The paper tackles joint extraction of named entities and relations without hand-crafted features by extending a BiLSTM-CRF model with a deep biaffine attention layer, achieving new state-of-the-art results on the CoNLL04 dataset.
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.