# A Neural N-Gram-Based Classifier for Chinese Clinical Named Entity Recognition
## 2 Related Work  
Since the growth of EMRs has increased considerably over recent decades, the CNER problem has drawn much interest and a great deal of research effort has been devoted to the study and development as well. Basically, four representative types of methods are proposed to perform the task which include rule-based, dictionary-based, machine learning and deep learning approaches [9; 10; 11].  
In the early stage, rule-based approaches were the dominant approaches to solve the CNER problem. The adoption of heuristic information, handcrafted features [12; 13] and lexical resources [14; 15] is designed to detect clinical entities. Although playing a critical role before, rule-based approaches heavily require expert domain knowledge, resulting in being difficult to transfer to different fields.  
Traditionally, dictionary-based methods take advantage of existing clinical vocabularies to extract entities and were widely applied due to their simplicity [15; 16]. Several clinical ontologies and vocabularies, such as MeSH [17] and SNOMED-CT [18], have been proposed. However, the performance is limited by the size of the lexicon and could lead to low recall when the input data contains a high number of out-of-dictionary entities. Additionally, similar to the rule-based approaches, dictionary-based approaches also lack generalizability and require tremendous human effort to build the lexicons.  
Since machine learning methods have been successfully used for sequence labelling tasks such as POS tagging, NER and chunking, the CNER task has also been transformed into a sequence labelling problem and is solved by various machine learning algorithms. Typically, feature engineering is performed on the input sentence to convert the data into numerical representation. Three typical supervised sequence tagging models (HMM, MEMM, and CRF) based on n-gram and position features are evaluated for the name recognition in traditional Chinese clinical records where CRF achieves better performance than the other two classifiers [19]. Support Vector Machine (SVM) with word shape and part-of-speech features is applied to recognize biomedical named entity and obtains a precision rate of 84.24% and a recall rate of 80.76% [20]. However, most of these machine learning methods rely on pre-defined features (such as lexical, syntactic, and semantic features) and are difficult to generalize to different datasets.  
In recent years, since deep learning techniques are growing fast and have achieved significant success across various applications, the prevailing approaches have shifted to the employment of deep learning methods. The Long Short-Term Memory (LSTM) network is suitable for learning temporal relations and has been widely used in NLP tasks [21]. A BiLSTM-CRF approach, which is a neural network system based on bidirectional LSTMs and CRF, is proposed to solve the Chinese CNER problem using specialized word embeddings as feature representations and the external health domain lexicons as the knowledge base [22]. The system reports 87.95% in F-score on the CCKS-2017 (task 2) CNER dataset. Another bidirectional RNN-CRF model for recognizing Chinese CNER adopts concatenated n-gram embeddings and also includes word segmentation information, part-of-speech tagging and medical entity vocabulary as additional features [23]. Unlike the previous research relying on other miscellaneous information, in this paper, we present a neural n-gram based classifier without external resources.