A Hierarchical Framework for Relation Extraction with Reinforcement Learning


  • Ryuichi Takanobu Tsinghua University
  • Tianyang Zhang Tsinghua University
  • Jiexi Liu Tsinghua University
  • Minlie Huang Tsinghua University




Most existing methods determine relation types only after all the entities have been recognized, thus the interaction between relation types and entity mentions is not fully modeled. This paper presents a novel paradigm to deal with relation extraction by regarding the related entities as the arguments of a relation. We apply a hierarchical reinforcement learning (HRL) framework in this paradigm to enhance the interaction between entity mentions and relation types. The whole extraction process is decomposed into a hierarchy of two-level RL policies for relation detection and entity extraction respectively, so that it is more feasible and natural to deal with overlapping relations. Our model was evaluated on public datasets collected via distant supervision, and results show that it gains better performance than existing methods and is more powerful for extracting overlapping relations1.




How to Cite

Takanobu, R., Zhang, T., Liu, J., & Huang, M. (2019). A Hierarchical Framework for Relation Extraction with Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 7072-7079. https://doi.org/10.1609/aaai.v33i01.33017072



AAAI Technical Track: Natural Language Processing