基于深度强化学习的文本实体关系抽取方法

从文本大数据中快速准确地抽取文本的实体关系信息是构建知识图谱的关键.针对目前主流的远程监督关系抽取方法常常忽略实体对的类型信息和句子语法信息的问题,该文提出了一种基于深度强化学习的文本实体关系抽取方法.首先,利用结合实体周围词注意力机制的双向长短期记忆网络作为句子编码的第一个模块;然后,在此基础上加入实体类型嵌入模块,...

Entity Relationship Extraction from Text Data Based on Deep Reinforcement Learning

Extracting entity relationship information from text big data quickly and accurately is very important to build knowledge maps. The existing main methods for remote supervised relationship extraction often ignore the type information and syntactic information of entity pairs. In this work, a bi-directional long short-term memory (BiLSTM) model combined with an attention mechanism layer of words around entities is utilized as the first module of sentence encoding. Then, an entity type embedding module is added to the model to enrich sentence encoding information. Finally, a semantic dependency parsing module is also included to the model. Thus, the three modules form a relation extractor. In addition, most of distant supervised relationship extraction models are designed to reduce noises in packets and sentences, they ignore the impacts of noise labels on model performances. Focused on noise reduction of labels, this work designs a label learner, which can learn soft labels of sentences on the basis of reinforcement learning so as to modify noisy labels. A novel relationship extraction framework for text entities based on deep reinforcement learning is built from our designed relationship extractor and label learner. The experiment results for a self-constructed dataset and two public datasets, ACE2005 and Chinese-Literature-NER-RE-Dataset show that our proposed method outperforms several state-of-the-art models in precision and recall rate.