Abstract:  Entity relation extraction is an important sub-task of information extraction. It is of great significance for the construction of semantic knowledge base and the development of knowledge graph. For Chinese, semantic relations are more complex, and the effect of entity relation extraction is more significant. So discussing the details of Chinese entity relation extraction methods is very necessary. From the beginning of the emergence and development of entity relation extraction, the current status of Chinese entity relation extraction technology is discussed. Relation extraction methods can be divided into four categories according to the degree of dependence on the corpus: entity supervised relation extraction, unsupervised relation extraction, semi-supervised relation extraction and open domain relation extraction. This paper analyzes and compares these four methods. Finally, the application results and development prospects of deep learning in Chinese entity relation extraction are introduced.

Key words: Chinese entity relation extraction, supervised method, unsupervised method, semi-supervised method, open domain entity relation extraction method, deep learning