Incorporating Bidirection-Interactive Information and Semantic Features for Relational Facts Extraction (Student Abstract)

Abstract

The interaction between named entity recognition and relation classification is quite essential for the extraction of relational triplets. However, most of jointly extraction works only consider unidirectional interaction between the two sub-tasks. They even neglect the interactive information totally. In order to tackle these problems, we propose a novel unified joint extraction model which considers bidirection-interactive information between the two subtasks. Our model consists of two modules. The first module utilizes Bi-LSTM and GCN to capture the sequential and the structure-semantic features of a sentence, The second module utilizes two layers to capture bidirection-interactive information between the two subtasks and generates relational triplets respectively. The experimental results show that our proposed model outperforms the state-of-the-art models on two public datasets.