## 1 Introduction  
Due to the rapid proliferation of electronic medical records and the varied data formats they encompass, individuals are encountering growing difficulties in the pursuit of clinical knowledge. Typically, electronic medical records encompass a wide range of data types, including hospitalization records, medical procedure records, and more, collectively chronicing a patient's entire treatment history. It holds a crucial position in medical decision-making and research. Against this background, creating an intelligent named entity recognition (NER) system to identify medical entities with crucial information provides a solid foundation for constructing electronic medical record clinical knowledge bases and promoting knowledge discovery [1].  
NER constitutes the initial critical stage in the realm of natural language processing for information extraction [2]. Compared to general-domain texts, electronic medical record texts contain more domain-specific terminologies. For the electronic medical record NER task, the entity categories to be identified include anatomical location, disease diagnosis, symptom description, etc. Our purpose is to recognize these types of entities from clinical sentences. The formalization process of NER can be defined as follows: given an annotated sequence \(S=<w_{1},w_{2},\ldots,w_{n}>\), we can obtain a list of triples after the recognition, where each triple contains the information of an entity [3]. For example, in the triple \(<I_{s},I_{e},t>\), \(I_{s}\in[1,n]\) and \(I_{e}\in[1,n]\) respectively refer to the start index and the end index of an entity, and \(t\) is one of the predefined entity types. Figure 1 shows an example of the NER process. The input is a Chinese electronic medical record, \(\includegraphics[width=14.226378 pt, width=14.226378 pt]{  
a word-level representation with the fusion of SoftLexicon and RoBERTa, subsequently improving the recognition of long-distance entities using Bidirectional Long Short-Term Memory (BiLSTM). Finally, a Conditional Random Field (CRF) is employed for decoding. The main contributions of this paper can be summarized as follows:  
* We designed an SLRBC NER model for Chinese electronic medical records, fusing SoftLexicon and RoBERTa at the representation layer and adopting the classical BiLSTM-CRF framework to improve the model's performance;
* SLRBC employs a novel purpose-driven DIKW architecture. Within the SoftLexcion representation, we established four sets for each character to incorporate word lexicon information from electronic medical records into character representations and assigned weights to achieve a more comprehensive purpose representation;
* We conducted extensive experiments on the CCGS2018 and CCGS2019 public datasets to verify the effectiveness of SLRBC, and the results demonstrate its superiority in Chinese electronic medical record NER. The code can be accessed at [[https://github.com/QuXiaolong0812/SLRBC](https://github.com/QuXiaolong0812/SLRBC)]([https://github.com/QuXiaolong0812/SLRBC](https://github.com/QuXiaolong0812/SLRBC)) (accessed on 8 December 2023).  
The rest of this paper is organized as follows. In Section 2, we present an overview of the literature covering character-based, word-based, and hybrid methods. Section 3 details our proposed approach, encompassing the representation layer, the encoding layer, and the label decoding layer. Section 4 includes our experimental procedures and a discussion of the results. Section 5 discusses strengths and weaknesses of the proposed approach. Finally, in Section 6, we summarize our findings and provide insights into potential avenues for future research.