Reinforcement Learning for Joint Extraction of Entities and Relations

Entity and relation extraction is an important task in natural language processing (NLP). Most existing researches handle this issue in a pipelined work or joint learning methods relied on human-annotated corpora, which are vulnerable to errors cascading. On the other side, in order to obtain large training data for methods of supervised learning, distant supervision are used in previous work whereas largely suffer from noisy labeling problem. To solve these problems, we propose a reinforcement learning framework for joint extraction of entities and relations. First, we construct a relation extractor based on a tagging scheme to extract entities and relations jointly. Meanwhile, a data cleaner is designed to select high-quality sentences and feed them into relation extractor, by means of cleaning noisy sentences generated by distant supervision hypothesis. Afterwards, the two modules are trained jointly with reinforcement learning to optimize models. In experiments, our model achieved better performance than comparative methods on the public dataset.