## 1 Introduction  
Relation Extraction (RE) is an important subtask of information extraction [1]. With the rapid development of biomedical field, this technology has become a powerful tool for mining biological information and its relation. One of the key issues that biomedical relation extraction needs to address is how to obtain effective biomolecular information and its correlation relation from discrete yet interrelated massive data. It plays a very important role in downstream tasks such as medical knowledge graph construction and biomedical knowledge discovery [2].  
Traditional relation extraction methods are mainly rule-based methods and traditional machine learning-based methods [3], which mainly match text by handwritten rules or rely on manually designed kernel functions [4]. In recent years, with the gradual maturity of deep learning, deep learning models have made certain progress in relation extraction task, which no longer need to rely on professionals to design grammars and data features as before. The most important feature of deep learning is that it can automatically learn features in the data and can back-propagation learning. The use of neural network modelsfor relation extraction is not only simple and effective, but also promises to incorporate more diverse and richer knowledge into the relation extraction model. Among all the different sources of knowledge, syntactic information, especially dependency syntax tree, has been proven to be beneficial in many studies. Dependency syntax tree provides better semantic guidance for analyzing the contextual information associated with a given entity. However, the extensive use of dependency syntactic information does not always lead to good relation extraction performance, and the noisy information in it may bring confusion to relation classification. Especially when the sentence length in biomedical data is longer and the semantics are more complex, the dependency tree generated through sentences is too cumbersome, which not only increases the complexity of the model, but also reduces the performance of biomedical relation extraction in complex text data.  
In response to previous research and current realities, we summarize the following: First, problem statement: most of the biomedical texts have longer sentence lengths, which are more implicit in describing the relation between specialized entities. The sentence semantics are more complex. The current research on introducing dependency trees for relation extraction failed to solve the problem of noise information propagation in dependency trees. Second, research questions: how to solve the problem of dependency tree noise propagation and how to utilize dependency information to improve the performance of the model for biomedical relation extraction. Finally, hypothesis: the problem of noise propagation caused by dependency trees can be solved by necessary pruning strategies. The performance of the model's relation extraction can be improved by incorporating the dependency information between words.  
Based on the above questions and hypothesis, this paper proposes a method to improve the performance of relation extraction in biomedical domain from the perspective of dependency information. The main work is as follows:  
1. Obtain the dependency syntax tree of biomedical data labeled with entities and propose to use a local-global pruning strategy to filter out interfering information and improve accuracy;
2. Construct dependency type matrix for dependency information fusion on the pruned dependency tree so as to incorporate dependency information into the graph convolutional model. Calculating attention weights allows the model to distinguish the important dependency types in the dependency tree for predicting relations, thus improving the relation extraction performance.