# Named Entity Recognition in Online Medical Consultation Using Deep Learning
## 2 Related Work  
Named entity recognition constitutes an essential foundational task in natural language processing for building domain-specific knowledge graphs and developing virtual intelligent question-answering systems. Early named entity recognition methods were mainly based on complex human predefined rules and domain-specific dictionary matching. The extraction efficiency of specific information is higher; however, the generalization ability is poor. Adapting to the massive new vocabulary that is emerging in the mobile internet era and the high intellectual costs of domain-specific experts remain challenging [9]. [PERSON] et al. developed many regular expression-based matching rules to extract core medical concepts from discharge summaries [10]. [PERSON] et al. formulated a series of rules with different properties to detect named entities in biological texts [11]. With the initial progress in computer software and hardware technology, machine learning-based named entity recognition methods represented by CRF and hidden Markov methods have gradually begun to increase. The shortcomings of rule- and dictionary-based approaches have been somewhat mitigated. However, in the modeling stage, eliminating the reliance on manual work remains challenging. Before training the machine learning model, manual feature engineering is needed; that is, a series of features that can effectively characterize the data characteristics of a specific dataset must be extracted. [PERSON] combined a variety of traditional and novel features to identify five types of entities in biomedical abstracts using CRF [12]. [PERSON] et al. employed a modified version of the conventional hidden Markov model to detect nonrecursive entities within texts [13]. In the past ten years, breakthroughs have been made in computer software and hardware technology, and computing power has greatly increased. In particular, NVIDIA's ongoing advancements in CUDA technology have significantly facilitated the emergence of deep learning-driven named entity recognition techniques based on neural networks. The novel end-to-end learning method has completely removed the dependence on manual labor in the modeling stage; the model is capable of autonomously adapting and extracting relevant features based on the contextual information. [PERSON] et al. explored the feasibility of an iterated dilated CNN for named entity recognition and proposed the ID-CNN-CRF model based on this approach [14]. [PERSON] et al. applied the combination of BiLSTM and CRF to the natural language processing sequence annotation task for the first time, and the performance of entity recognition was improved by effectively using past and future input features [15]. On the basis of the study by [PERSON] et al., [PERSON] et al. further introduced a character-based word representation model [16] and a pretrained distributional representation model [17] to capture spelling and morphological information, effectively improving the entity recognition performance [18]. Building on this foundation, [PERSON] and [PERSON] developed a combination of CNN, BiLSTM, and CRF, proposing the CNN-BiLSTM-CRF model, which effectively enhances entity recognition performance [19]. Based on the study of [PERSON] and [PERSON], [PERSON] and [PERSON] proposed the fusion multi-feature-CNN-BiLSTM-CRF model. By designing a novel word embedding algorithm that effectively integrates pinyin, radical, and word meaning features at the model's word embedding layer, they successfully addressed the limitations of existing word embedding methods, leading to further improvements in entity recognition performance [6]. [PERSON] et al. proposed the W2V-ELMo-CNN-BiLSTM-CRF model to enhance the performance of Shahmukhi named entity recognition [20]. [PERSON] et al. proposed a word segmentation method based on dynamically updating a custom dictionary, and on this basis, they developed the BiLSTM-CRF-Loaded model, which effectively improved the performance of named entity recognition in Traditional Chinese Medicine chest discomfort cases [21].