A Deep Neural Network Model for Joint Entity and Relation Extraction

Yihe Pang, Jie Liu, Lizhen Liu, Zhengtao Yu, Kai Zhang

Abstract: Joint extraction of entities and their relations from the text is an essential issue in automatic knowledge graph construction, which is also known as the joint extraction of relational triplets. The relational triplets in sentence are complicated, multiple and different relational triplets may have overlaps, which is commonly seen in reality. However, multiple pairs of triplets cannot be efficiently extracted in most of the previous works. To mitigate this problem, we propose a deep neural network model based on the sequence-to-sequence learning, namely, the hybrid dual pointer networks (HDP), which extracts multiple pairs of triplets from the given sentence by generating the hybrid dual pointer sequence. In experiments, we tested our model using the New York Times (NYT) public dataset. The experimental results demonstrated that our model outperformed the state-of-the-art work, and achieved a 17.1% improvement on the F1 values.

External IDs:dblp:journals/access/PangLLYZ19