Chen Tingyin, Wu Xuehong, Li Linyi, Li Jianhua, Feng Song
Department of Network and Information, Xiangya Hospital, Central South University, Changsha, China.
National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Changsha, China.
Ann Transl Med. 2022 May;10(9):520. doi: 10.21037/atm-22-1226.
Entity relation extraction technology can be used to extract entities and relations from medical literature, and automatically establish professional mapping knowledge domains. The classical text classification model, convolutional neural networks for sentence classification (TEXTCNN), has been shown to have good classification performance, but also has a long-distance dependency problem, which is a common problem of convolutional neural networks (CNNs). Recurrent neural networks (RNN) address the long-distance dependency problem but cannot capture text features at a specific scale in the text.
To solve these problems, this study sought to establish a model with a multi-scale convolutional recurrent neural network for Sentence Classification (TEXTCRNN) to address the deficiencies in the 2 neural network structures. In entity relation extraction, the entity pair is generally composed of a subject and an object, but as the subject in the entity pair of medical literature is always omitted, it is difficult to use this coding method to obtain general entity position information. Thus, we proposed a new coding method to obtain entity position information to re-establish the relationship between subject and object and complete the entity relation extraction.
By comparing the benchmark neural network model and 2 typical multi-scale TEXTCRNN models, the TEXTCRNN [bidirectional long- and short-term memory (BiLSTM)] and TEXTCRNN [double-layer stacking gated recurrent unit (GRU)], the results showed that the multi-scale CRNN model had the best F1 value performance, and the TEXTCRNN (double-layer stacking GRU) was more capable of entity relation classification when the same entity word did not belong to the same entity relation.
The experimental results of the entity relation extraction from showed that entity relation extraction could effectively proceed using the new labeling method. Additionally, compared to typical neural network models, including the TEXTCNN, GRU, and BiLSTM, the multi-scale convolutional recurrent neural network structure had advantages across several evaluation indicators.
实体关系提取技术可用于从医学文献中提取实体和关系，并自动建立专业的映射知识领域。经典的文本分类模型，即用于句子分类的卷积神经网络（TEXTCNN），已被证明具有良好的分类性能，但也存在长距离依赖问题，这是卷积神经网络（CNN）的常见问题。循环神经网络（RNN）解决了长距离依赖问题，但无法捕捉文本中特定尺度的文本特征。
为了解决这些问题，本研究试图建立一种用于句子分类的多尺度卷积循环神经网络模型（TEXTCRNN），以解决这两种神经网络结构的不足。在实体关系提取中，实体对通常由一个主语和一个宾语组成，但由于医学文献实体对中的主语总是被省略，因此难以使用这种编码方法来获得通用的实体位置信息。因此，我们提出了一种新的编码方法来获取实体位置信息，以重新建立主语和宾语之间的关系并完成实体关系提取。
通过比较基准神经网络模型和两种典型的多尺度TEXTCRNN模型，即TEXTCRNN [双向长短期记忆（BiLSTM）]和TEXTCRNN [双层堆叠门控循环单元（GRU）]，结果表明多尺度CRNN模型具有最佳的F1值性能，并且当相同的实体词不属于同一实体关系时，TEXTCRNN（双层堆叠GRU）在实体关系分类方面更具能力。
实体关系提取的实验结果表明，使用新的标注方法可以有效地进行实体关系提取。此外，与包括TEXTCNN、GRU和BiLSTM在内典型神经网络模型相比，多尺度卷积循环神经网络结构在几个评估指标上具有优势。
