An Entity-Aware Adversarial Domain Adaptation Network for Cross-Domain Named Entity Recognition (Student Abstract)

Authors

  • Qi Peng 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
  • Changmeng Zheng 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
  • Tao Wang Department of Biostatistics and Health Informatics, King's College London
  • Haoran Xie Department of Computing and Decision Sciences, Lingnan University, Hong Kong, China
  • Qing Li Department of Computing, Hong Kong Polytechnic University, Hong Kong, China

Keywords:

Named Entity Recognition, Domain Adaptation, Cross Domain, Adversarial Training, Entity-aware Attention

Abstract

Existing methods for named entity recognition (NER) are critically relied on the amount of labeled data. However, these methods suffer from performance decline in a new domain which is fully-unlabeled. To handle the situation, we propose an entity-aware adversarial domain adaptation network, which utilizes the labeled data from source domain and then adapts to unlabeled target domain. We first apply adversarial training to reduce the distribution gap between different domains. Furthermore, we introduce an entity-aware attention to guide adversarial to achieve the alignment of entity features. The experimental results show that our model outperforms the state-of-the-art approaches.

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Published

2021-05-18

How to Cite

Peng, Q., Zheng, C., Cai, Y., Wang, T., Xie, H., & Li, Q. (2021). An Entity-Aware Adversarial Domain Adaptation Network for Cross-Domain Named Entity Recognition (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15865-15866. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17929

Issue

Section

AAAI Student Abstract and Poster Program