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

Authors

  • Yang Yu School of Software Engineering, South China University of Technology, Guangzhou, China Key Laboratory of Big Data and Intelligent Robot (South China University of Technology), Ministry of Education
  • Guohua Wang School of Software Engineering, South China University of Technology, Guangzhou, China Key Laboratory of Big Data and Intelligent Robot (South China University of Technology), Ministry of Education
  • Haopeng Ren School of Software Engineering, South China University of Technology, Guangzhou, China Key Laboratory of Big Data and Intelligent Robot (South China University of Technology), Ministry of Education
  • Yi Cai School of Software Engineering, South China University of Technology, Guangzhou, China Key Laboratory of Big Data and Intelligent Robot (South China University of Technology), Ministry of Education

Keywords:

Relational Facts Extraction, Bidirection-Interactive Information, Semantic Features

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.

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Published

2021-05-18

How to Cite

Yu, Y., Wang, G., Ren, H., & Cai, Y. (2021). Incorporating Bidirection-Interactive Information and Semantic Features for Relational Facts Extraction (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15947-15948. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17970

Issue

Section

AAAI Student Abstract and Poster Program