Recursively Binary Modification Model for Nested Named Entity Recognition


  • Bing Li University of New South Wales
  • Shifeng Liu University of New South Wales
  • Yifang Sun University of New South Wales
  • Wei Wang University of New South Wales
  • Xiang Zhao National University of Defence Technology



Recently, there has been an increasing interest in identifying named entities with nested structures. Existing models only make independent typing decisions on the entire entity span while ignoring strong modification relations between sub-entity types. In this paper, we present a novel Recursively Binary Modification model for nested named entity recognition. Our model utilizes the modification relations among sub-entities types to infer the head component on top of a Bayesian framework and uses entity head as a strong evidence to determine the type of the entity span. The process is recursive, allowing lower-level entities to help better model those on the outer-level. To the best of our knowledge, our work is the first effort that uses modification relation in nested NER task. Extensive experiments on four benchmark datasets demonstrate that our model outperforms state-of-the-art models in nested NER tasks, and delivers competitive results with state-of-the-art models in flat NER task, without relying on any extra annotations or NLP tools.




How to Cite

Li, B., Liu, S., Sun, Y., Wang, W., & Zhao, X. (2020). Recursively Binary Modification Model for Nested Named Entity Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8164-8171.



AAAI Technical Track: Natural Language Processing