# An Improved Method for Named Entity Recognition and Its Application to CEMR+
## 1 Introduction  
With the rapid development of the medical industry, data mining on Clinical Electronic Medical Records (CEMR) plays an important role in precision medicine. It provides basic technology for subsequent medical record summaries, computer auxiliary diagnosis, etc. The state-of-the-art Named Entity Recognition (NER) methods use Long Short-Term Memory (LSTM) to extract features and then employ the Conditional Random Field (CRF) to obtain the optimal tag sequence [1; 2; 3; 4]. However, the temporal structure of LSTM-based models is usually computationally expensive and inefficient, especially when faced with massive medical records. The output of the current time of the LSTM model depends on the output of the previous moment, which results in the inability to calculate in parallel. The CNN model tends to have a faster prediction time than the LSTM model of the temporal structure. People often have to make a choice between the excellent performance and low efficiency of the LSTM model on the NLP task [5; 6]. This would be very beneficial if the CNN model directly replaces the LSTM structure and achieves similar performance to the LSTM on NLP tasks. Recent works [7; 8] attempted to apply Convolutional Neural Networks (CNN) to NER. Most of the deep learning networks are based on the convolutional neural network (CNN) architecture because it can make better use of the GPU and thus have a great performance in terms of speed. Nevertheless, the performance of CNNs-based models is poorer than LSTMs-based models due to it neglecting theglobal semantic information. Recently, the Iterated Dilated CNNs (ID-CNNs) [9] were proposed to efficiently aggregate a broad context. The comparison between the IDCNN model and the traditional Bi-LSTM model structure can be seen in Figure 1. However, this model ignores the significance of the word-order feature and local context in the text. As shown in Figure 1, the natural timing structure of the traditional Bi-LSTM model enables location information to be captured. The ID-CNNs model has no time structure. It cannot capture the relative relationship between words and ignores word-order information. For example, swapping the positions of \(x_{1}\) and \(x_{2}\) in Figure 1 has a large impact on the Bi-LSTM model. For the ID-CNN model, the output is not affected.  
To address these issues, we propose an attention-based ID-CNNs-CRF model. We first introduce position embedding to capture word-order information. Then, the attention mechanism is applied to the ID-CNNs-CRF model because of its good performance in NLP tasks, which enables the enhancement of the influence of critical words. Finally, we apply the CRF to obtain the optimal tag sequence. Experimental results on two CEMR datasets demonstrate that the proposed model achieves better prediction performance with a higher F1-score than those of baseline methods.  
The remainder of the paper is structured as follows: Section 2 introduces the related work. Section 3 describes the details of the proposed method. Section 4 demonstrates the proposed methodology with a series of experiments. Finally, the conclusions of this study are given in Section 5.