A Relationship Extraction Framework Based on Reinforcement Learning and Machine Reading Comprehension

The task of relation extraction is to extract semantic relationships between entities from text, which is an important element in the fields of information extraction, natural language understanding, and information retrieval. With the application of distant supervision, deep learning, and other technologies, relation extraction has made remarkable progress, but there are still problems such as insufficient data volume and inability to learn sentence representations. To deal with these challenges, we propose a framework called RLMRC-RE (Reinforcement Learning and Machine Reading Comprehension-Relationship Extraction) for jointly performing reinforcement learning and machine reading comprehension-based relationship extraction. Experiments were conducted on two distant supervision datasets, and the results were compared with several relevant models in recent years. The findings demonstrate that the model proposed in this paper effectively learns text representations while mitigating the impact of noise.