A Nested Named Entity Recognition Model Based on Multi-agent Communication Mechanism (Student Abstract)

Authors

  • Canguang Li 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
  • Jin Cao 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:

Information Extraction, Multiagent Systems, Knowledge Discovery

Abstract

Traditional sequence tagging methods for named entity recognition (NER) face challenges when handling nested entities, where an entity is nested in another. Most previous methods for nested NER ignore the effect of entity boundary information or type information. Considering that entity boundary information and type information can be utilized to improve the performance of boundary detection, we propose a nested NER model with a multi-agent communication module. The type tagger and boundary tagger in the multi-agent communication module iteratively utilize the information from each other, which improves the boundary detection and the final performance of nested NER. Empirical experiments conducted on two nested NER datasets show the effectiveness of our model.

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Published

2021-05-18

How to Cite

Li, C., Wang, G., Cao, J., & Cai, Y. (2021). A Nested Named Entity Recognition Model Based on Multi-agent Communication Mechanism (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15823-15824. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17908

Issue

Section

AAAI Student Abstract and Poster Program