# An Improved Method for Named Entity Recognition and Its Application to CEMR+
## 2 Related Work  
Benefiting from the implementation of digital medicine, the digitization of medical records has been promoted for many years. Named Entity Recognition (NER) of CEMR designed to identify critical entities of interest is a basic step in medical information extraction. Traditionally, many simple, but straightforward methods, such as rule-based and heuristic-search-based methods, have been utilized to identify critical medical entities [10]. These methods tend to achieve a low recall value due to the inability to cover all medical entities. Although rule-based methods seem better than dictionary-based methods, large numbers of rules require extensive domain knowledge to be formulated by medical professionals [11]. With the expansion of medical data, these time-consuming and laborious original methods seem to be clumsy. However, these approaches still exist because they can be utilized as part of other systems to achieve good performance [12; 13]. NER methods based on statistical machine learning include Support Vector Machine (SVM) [14], the Hidden Markov Model (HMM) [15; 16], the Maximum Entropy Hidden Markov  
Figure 1: The left figure shows the traditional Bi-LSTM model. The input of the current moment of the Bi-LSTM model depends on the output of the previous moment, so at time \(i\), the calculation must be iterative from left to right and iteratively calculated from right to left. The figure on the right is the Iterated Dilated (ID)-CNNs model. It is similar to the CNN structure and can be calculated in parallel. In addition, the receptive field is expanded between layers, so that global semantic information can be obtained. Model (MEHMM) [17], and the Conditional Random Field (CRF) [18]. [PERSON] et al. [16] presented an HMM NER system to deal with the special phenomena in the biomedical domain. [PERSON] et al. [19] utilized a CRF-based machine learning system named Nersuite to achieve an F-score of 88.46% on the Gellus corpus. The CRF methods have decent performance, but rely heavily on the selection of features. At present, the CRF-based medical named entity recognition method is the best method in statistical machine learning because of the consideration of the transfer between tags. These methods do not need to match a large number of medical entity dictionaries, nor do experts need to make rules for entity boundaries, but rely heavily on feature selection. These features such as Parts-Of-Speech (POS), lexical features, capitalization, etc., need linguists and domain experts to formulate them, which means that feature engineering is required. In recent years, deep learning methods have been developed for NER. LSTM architectures that are capable of learning the long-term dependencies have been put forward [20], especially bidirectional recurrent neural network architectures [20; 21; 22]. [PERSON] et al. [1] utilized bidirectional LSTMs and conditional random fields to obtain improvement in NER in multiple languages without resorting to any language-specific knowledge. [PERSON] et al. [3] utilized NeuroCRF to obtain an F1-score of 89.28% on the WikiNER dataset. [PERSON] et al. proposed a neural network approach named attention-based Bi-LSTM-CRF for document-level chemical NER. The approach leverages document-level global information obtained by the attention mechanism to enforce tagging consistency across multiple instances of the same token in a document [23]. However, they are inefficient because of their sequential processing on sentences. To alleviate this problem, [PERSON] et al. [7] applied the ID-CNNs architecture to speed up the processing of the network. They proved that the test-time speed of the ID-CNN models was 1.42-times faster than the Bi-LSTM models. However, these models tend to ignore the word-order feature and local context compared to LSTM-based models. As shown in Figure 1, the ID-CNN model failed to take advantage of word-order information between words, so the model itself is more like a bag of words model.