Papers › End-to-end neural relation extraction using deep biaffine attention

End-to-end neural relation extraction using deep biaffine attention

29 Dec 2018arXiv:1812.11275archive 2025-07-28

Dat Quoc Nguyen, Karin Verspoor

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.

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