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.
CITATION STYLE
Nguyen, D. Q., & Verspoor, K. (2019). End-to-end neural relation extraction using deep biaffine attention. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11437 LNCS, pp. 729–738). Springer Verlag. https://doi.org/10.1007/978-3-030-15712-8_47
Mendeley helps you to discover research relevant for your work.