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

Dat Quoc Nguyen; Karin Verspoor

doi:10.1007/978-3-030-15712-8_47

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arXiv · 1812.11275

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

Dat Quoc Nguyen·Karin Verspoor

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Abstract

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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BibTeX RIS

Dat Quoc Nguyen, Karin Verspoor. 2018-12-29. End-to-end neural relation extraction using deep biaffine attention. https://doi.org/10.1007/978-3-030-15712-8_47

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