Incorporating Human Knowledge in Neural Relation Extraction with Reinforcement Learning
Bing Liu, Guilin Qi, Lu Pan, Shangfu Duan, Tianxing Wu · 2019
Relation Extraction (RE) aims at extracting semantic relation of entities from text and it is a crucial task in natural language processing. Deep neural network (DNN) based models have achieved excellent performance in RE. However, they still have several problems remaining to be addressed: (1) humans can hardly take measures to amend the DNN-based RE systems because it is difficult to encode human intention to guide them to capture desired patterns. (2) DNN-based RE models may suffer from not having sufficient background information for making predictions. To handle these issues, we propose an RE framework based on reinforcement learning, which can enhance existing DNN-based RE models by incorporating human knowledge including soft rules and relation evidence. The introduction of soft rules enable human to impose an effect on the RE result and correct the RE system, while the relation evidence help supplement the background information without limiting its types and sources. The experimental results show that our approach can reinforce existing DNN-based RE models effectively and outperforms state-of-the-art RE methods.