# Medical Named Entity Recognition Fusing Part-of-Speech and Stroke Features
## 2 Related Work
### NER Approaches Based on Deep Learning  
The embedding layer, encoding layer, and decoding layer are the three components of deep learning-based NER methods. The embedding layer learns the semantic and grammatical information of the input text and displays them as a vector. The encoding layer performs feature extraction according to the contextual data incoming from the embedding layer. The decoding layer predicts the label corresponding to each word in the text according to the encoding layer's results.  
For the embedding layer, the full input Chinese text must be segmented. Word-based and character-based segmentation processing techniques are the two main categories. With the word-based approach, the text is divided into words, and the pertinent information in the words is used to extract contextual characteristics. However, issues will unavoidably arise throughout the word segmentation phase, and the mistakes created during this process will be conveyed step by step in the subsequent recognition process, impacting the final recognition result. The character-based model uses the character as its input, does not require splitting words in the text, avoids the error introduced by word separation, and frequently outperforms the word-based model, though words still have more detailed boundary and semantic information.  
In the encoding layer, CNN, Recurrent Neural Network (RNN), and Transformer networks are frequently employed. The CNN-based model concentrates on extracting local features and can be calculated in parallel to shorten training time and save time and cost. CNN, however, is unable to record distant characteristics and ignores contextual data. [PERSON] et al. [29] introduced a CNN-based biomedical NER method with a multi-layer structure that uses neural networks to produce a significant amount of possible feature data embodied in word vectors. Data from linear sequences are routinely processed using a model based on RNN. An RNN variation called BiLSTM is capable of using sequential data in both forward and backward directions to learn the connection between distant dependencies. The computation of the current element, however, depends on the calculation outcome of the preceding element owing to the sequential sequence; hence, simultaneous processing is not possible. [PERSON] et al. [30] introduced a multi-channel neural structure for social media messaging, which integrated whole-word representations with multi-channel data and conditional random fields into BiLSTM without the need for any extra hand-made features. The model based on Transformer can make use of attention mechanisms to better capture long-distance dependencies and parallel computation; however, it is unable to capture information such as direction and position. [PERSON] et al. [31] propose a multimodal NER approach using a Transformer as a foundation, and a multimodal interaction module may be used to gain the verbal description of picture perception as well as the visual depiction of verbal perception.  
CRF and Multi-Layer Perceptron (MLP) are commonly used in decoding layers. The Multi-Layer Perceptron views sequence labeling as a multi-classification issue as the decoding layer, and each word's prediction simply makes inferences based on context-related information rather than considering the predicted label outcomes of neighboring words. As the decoding layer, CRF considers the whole sequence, learns the relationships between nearby tags during training, and incorporates restrictions into the prediction to provide the ideal tag sequence.