## 1 Introduction  
With the rapid development of the web, the Internet is currently flooded with texts of all kinds, including news, chat conversations, professional books, etc. It will be of great value if we can discover valuable knowledge from this large web data. Knowledge extraction enables us to extract valuable knowledge from large text corpora quickly and accurately. Knowledge extraction includes two subtasks, named entity recognition and relation extraction. Named entity recognition and relation extraction are two core tasks that output structured representation in the form of relational tuples (Entity1, RelationWords, Entity2). For example, in the sentence \"[PERSON] and [PERSON] were born in Shang Hai and Guang Dong respectively\", we can extract the knowledge tuple: [\"Subject\": \"[PERSON]\", \"Predicate\": \"born in\", \"Object\": \"Shang Hai\"], (\"Subject\": \"[PERSON]\", \"Predicate\": \"born in\", \"Object\": \"Guang Dong\"]. These tuples can be used to construct a knowledge graph, which is further used in Q and A or recommendation systems. Although many knowledge extraction systems have achieved reasonable performance, especially in English data. However, traditional knowledge extraction systems have two main drawbacks. Firstly, some systems are carried out through a knowledge extraction pipeline; thus, the named entities are extracted first, and then the relationships are determined through relationship classification. This pipeline leads to error accumulation and affects the final end-to-end performance. On the other hand, some systems are only suitable for closed domain knowledge extraction; thus, the entity types and relationships are fixed. Therefore, such systems can not meet the requirements of open-domain knowledge extraction, where relations are massive and undefined in advance. For Chinese knowledge extraction systems, the development is slower than in English ones for two reasons. Firstly, there is a lack of Chinese annotated corpus. Especially when we use a neural network model, a large-scale training corpus is needed. In addition, the Chinese language is more sophisticated and flexible than English in morphology, syntax and grammar, which makes open-domain knowledge extraction more difficult [1]. There are more entity nesting and relation nesting issues in Chinese sentences. Knowledge extraction from Chinese biomedical text is more challenging because the sentence structure is complicated, and most of the sentences contain multiple relationships. According to [2], from a statistic analysis of 3000 sentences from Chinese biomedical text, about 66% of the sentences are multiple relation-bearing sentences. Traditional Chinese knowledge extraction systems are based on the results of Dependency Parsing (DP) [3] and then combine rules to extract relation tuples. With the development of deep learning, especially the successful application of pre-training language models such as BERT [4; 5] in the field of natural language understanding, more and more researchers have begun to adopt deep neural network models in knowledge extraction tasks. For fixed-domain relation extraction, the deep neural network model has made good achievements. In this paper, we studied how to extract open knowledge from unstructured Chinese text. Given a Chinese text fragment, our task is to extract entities and their relationships. Entity types and relationship types are not predefined. The formal problem definition is given in Section 3. Generally speaking, we faced two challenges. The first one is the nested entities and nested relationship issues, and we used a handshaking tagging scheme to alleviate this issue. Handshake tagging is very flexible, where it treats the predicate entity like the subject and object entity and solves the scenario of an unfixed relationship in the open domain.