## 2 Related Work  
Early research on relation extraction had problems such as time-consuming, laborious, and low accuracy. Existing studies show that deep learning-based relation extraction methods have become a research hotspot. In biomedical relation extraction, sentence-level relation extraction has become a research hotspot in recent years [5]. Domestic and foreign researchers have continuously innovated the relation extraction model architecture for the characteristics of texts in this field, promoting the research progress of relation extraction in the biomedical field. [PERSON] et al. [6] first used Convolutional Neural Network (CNN) for the relation extraction task. They utilized the powerful local feature extraction ability of convolutional neural network to extract features of words and sentences, and used position embedding to represent the distance between words and entities. [PERSON] et al. [7] used convolutional neural network for relation classification task which classifies by ranking. They also proposed a new pairwise ranking loss function so that the impact of artificial classes can be easily reduced. But these methods have poor performance when facing pairs of entities that are far away from each other. [PERSON] et al. [8] first proposed to apply Recurrent Neural Network (RNN) to relation extraction and made some progress. However, RNN may generate problems such as gradient disappearance or gradient explosion when processing long sequence text. [PERSON] et al. [9] proposed that RNN can be used to classify drug interactions in biomedical texts. The system is based on MV-RNN, a sentence tree based on Stanford constituencies. However, MV-RNN did not provide satisfactory results, mainly due to the fact that DDIs are usually described by long sentences with complex structures (e.g., subordinate clauses, antithetical and parallel structures, etc.). Later, Long Short-Term Memory (LSTM) was proposed to solve the problem of RNN. [PERSON] et al. [10] used bidirectional LSTM for relation extraction and achieved good improvement. [PERSON] et al. [11] proposed a graph state LSTM model that uses parallel states to model each word, continuously enriching the state values through message passing and speeding up the computation through more parallelization. At that time, their model performed well on the CIVIC dataset. [PERSON] et al. [12] showed that the relation extraction performance could be further improved by combining CNN with RNN. Later, the continuous development of attention mechanism and language models provided better semantic representation solutions for natural language processing tasks, and the birth of BERT pre-training model caused a significant repercussion in the field of NLP. [PERSON] et al. [13] used bidirectional LSTM combined with attention mechanism for relation extraction task, which could effectively learn the importance of words using attention mechanism. [PERSON] et al. [14] used graph neural network to combine sentence semantics to represent relations and incorporated attention mechanism to minimize the impact of incorrect labels in samples. [PERSON] et al. [15] used BERT for relation extraction task, which greatly improved the effectiveness and made new progress in stages. Existing research has extensively used syntactic information, especially dependency syntax tree, to improve relation extraction. [PERSON] and [PERSON] [16] have demonstrated that dependency syntax tree is beneficial for relation extraction and that dependency analysis is effective in capturing long-distance word relations in relation extraction tasks. However, over-utilization of dependency information can lead to confusion in the relation extraction models, so necessary pruning strategies are needed to alleviate this problem. For example, [PERSON] et al. [17] proposed to use only the shortest dependency path between two entities for the dependency connections and model them using LSTM. [PERSON] and [PERSON] proposed to prune the original dependency tree into the lowest common ancestor subtree.