A Hierarchical Framework for Relation Extraction with Reinforcement Learning

9 Nov 2018  ·  Ryuichi Takanobu, Tianyang Zhang, Jiexi Liu, Minlie Huang ·

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 relations.

PDF Abstract


Introduced in the Paper:


Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Relation Extraction NYT10-HRL HRL F1 64.4 # 9
Relation Extraction NYT11-HRL HRL F1 53.8 # 6
Relation Extraction NYT24 HRLRE F1 77.6 # 2
Relation Extraction NYT29 HRLRE F1 64.3 # 3


No methods listed for this paper. Add relevant methods here