###### Abstract  
Extracting entity relations from unstructured medical texts is a fundamental task in the field of medical information extraction. In relation extraction, dependency trees contain rich structural information that helps capture the long-range relations between entities. However, many models cannot effectively use dependency information or learn sentence information adequately. In this paper, we propose a relation extraction model based on syntactic dependency structure information. First, the model learns sentence sequence information by Bi-LSTM. Then, the model learns syntactic dependency structure information through graph convolutional networks. Meanwhile, in order to remove irrelevant information from the dependencies, the model adopts a new pruning strategy. Finally, the model adds a multi-head attention mechanism to focus on the entity information in the sentence from multiple aspects. We evaluate the proposed model on a Chinese medical entity relation extraction dataset. Experimental results show that our model can learn dependency relation information better and has higher performance than other baseline models. dependency information; graph convolutional networks; pruning operation; attention mechanism +
Footnote †: journal: Medical Informatics  
divide words according to the semantics. Errors in Chinese word cutting can greatly affect the results of relation extraction. At present, Chinese word-cutting methods include classical mechanical word-cutting [8], statistical word-cutting [9], and neural network methods [10]. For example, [PERSON] et al. [11] proposed a novel Chinese pre-training paradigm, Lattice-BERT, which explicitly combines word representations with characters so that sentences can be modeled in a multi-granularity manner. Currently, relation extraction methods can be divided into two categories: sequence-based and dependency-based. Sequence-based approaches use only word embeddings as input to the model, while dependency-based models merge dependency trees into the model. Graph convolutional networks (GCNs) can extract spatial features on topological graphs to learn the information on the whole graph [12]. In existing studies, syntactic information is also widely used in relation extraction tasks. Syntactic dependency relations have better semantic guidance and entity relation information of sentences [13]. Compared with the sequence-based approach, the dependency-based approach can better obtain non-local entity relation information from sentences. According to the above analysis, the Chinese medical relation extraction model needs to learn the sequence information of sentences. Apart from that, syntactic dependency information should be considered. Therefore, this paper proposes a Chinese medical relation extraction model, BAGCN (BiLSTM + Attention + GCN), based on syntactic dependency structure information. The model captures sentence-dependent structural information and sequence information through a graph convolutional neural network (GCN) and a bidirectional long short-term memory neural network (Bi-LSTM). In addition, we incorporate a new pruning operation in the model, considering the effect of noise on the dependency information. Finally, the model applies a multi-head attention mechanism to learn entity-related information from different perspectives. In this way, we make full use of the sequence information and dependency information of sentences to extract entity-relation triples. The main contributions of this paper are as follows:  
(1) The model constructs each sentence as syntactic dependency trees to learn the information of sentences. The dependency tree contains syntactic information and relation structures between words in a sentence. The hidden features of entity relations in the sentence can be fully explored by learning the dependency relations. (2) The model combines BiLSTM and GCN to extract feature information together.