A Hierarchical Framework for Relation Extraction with Reinforcement Learning
arXiv:1811.03925
Abstract
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.
arXiv commentTo appear in AAAI 19
References in corpus (5)
- Incorporating Copying Mechanism in Sequence-to-Sequence Learning
- End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
- Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
- Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning
- Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning